技术领域technical field
本发明涉及电力市场领域，尤其是涉及一种可再生能源参与中长期电力交易的电量分配方法。The invention relates to the field of electric power market, in particular to a power distribution method for renewable energy to participate in medium and long-term power trading.
背景技术Background technique
随着经济快速发展，能源和环境问题已成为当今世界所关注的焦点。煤炭、石油、天然气等能源的需求与日俱增，但这些能源不可再生，并且在利用过程中会对环境造成严重污染，对于社会的健康发展和稳定造成的影响也越来越大，因此可再生能源发电受到了越来越广泛地关注，风力和太阳能发电将成为我国未来能源结构的主力。With the rapid economic development, energy and environmental issues have become the focus of the world today. The demand for energy such as coal, oil, and natural gas is increasing day by day, but these energy sources are non-renewable, and will cause serious pollution to the environment during the utilization process, and the impact on the healthy development and stability of society is also increasing. Therefore, renewable energy power generation It has received more and more attention, and wind power and solar power will become the main force of my country's future energy structure.
在各国政府政策支持下，经过多年的研究，风电和光伏已经成为较为成熟的新能源发电技术。但由于风电和光伏发电本身的特性，将这两种可再生能源接入电力系统会给系统带来一定的挑战，与此同时，现阶段我国积极推进电力改革，电力系统已逐渐步入市场化阶段，但是目前还没有一种考虑到可再生能源参与的中长期电力规划方案，导致可再生能源的利用率下降，还会因信息不对称导致资源错配。With the support of government policies in various countries, after years of research, wind power and photovoltaics have become relatively mature new energy power generation technologies. However, due to the characteristics of wind power and photovoltaic power generation, connecting these two renewable energy sources to the power system will bring certain challenges to the system. At the same time, my country is actively promoting power reform at this stage, and the power system has gradually entered the marketization However, there is currently no mid-to-long-term power planning scheme that takes into account the participation of renewable energy, resulting in a decline in the utilization rate of renewable energy and resource misallocation due to information asymmetry.
发明内容Contents of the invention
本发明的目的就是为了克服上述现有技术存在的缺陷而提供一种可再生能源参与中长期电力交易的电量分配方法。The purpose of the present invention is to provide a power distribution method in which renewable energy participates in medium and long-term power trading in order to overcome the above-mentioned defects in the prior art.
本发明的目的可以通过以下技术方案来实现：The purpose of the present invention can be achieved through the following technical solutions:
一种可再生能源参与中长期电力交易的电量分配方法，包括以下步骤：A power distribution method for renewable energy participating in medium and long-term power trading, comprising the following steps:
1)根据能源市场的出清数据拟定发电企业的申报电价；1) According to the clearing data of the energy market, formulate the declared electricity price of the power generation enterprise;
2)根据二层规划理论，建立可再生能源参与中长期电力交易的二层规划模型；2) According to the two-level planning theory, establish a two-level planning model for renewable energy to participate in medium and long-term power transactions;
3)采用离散粒子群和连续粒子群相结合的混合算法以及非线性规划法对二层规划模型进行求解，根据得到最优解完成发电企业的电量和电力用户的电价的分配。3) A hybrid algorithm combining discrete particle swarm optimization and continuous particle swarm optimization and a nonlinear programming method are used to solve the two-level programming model, and the distribution of power generation enterprises' electricity and power users' electricity prices is completed according to the optimal solution.
所述的步骤2)中，可再生能源参与中长期电力交易的二层规划模型的以发电企业的负利润最小为目标函数建立上层规划模型，以社会福利最大为目标函数建立下层规划模型。In the step 2), the two-level planning model of renewable energy participating in medium and long-term power trading uses the minimum negative profit of power generation enterprises as the objective function to establish the upper-level planning model, and takes the maximum social welfare as the objective function to establish the lower-level planning model.
所述的上层规划模型的目标函数为：The objective function of the upper layer planning model is:
其中，Nc为发电企业/机组i的总数，T为时段总数，πi为发电企业的出清电价，ui,t为机组在时段t运行状态的0-1变量，分别代表关机和开机，Pi,t为机组在时段t应提供的输出功率，πsw为可再生能源发电企业的出清电价，PSW,t为风电场以及光伏电站在t时刻的总出力，为发电企业的单位发电成本，F(Pi,t)为机组在时段t的出力报价函数，Si,t为机组在时段t的启动成本，为机组在时段t提供备用容量时需要承担的费用，ai、bi、ci分别为机组i的报价函数的各项系数，αi为机组i的启动和检修成本，βi为冷启动成本，为机组已经停运的时间，λi为机组冷却速度，为排放单位CO2需支付的费用，为机组i在时段t对应于机组出力的CO2排放量，CD,i为机组i在时段t提供单位备用容量需支付的费用，Ri,t为机组i在时段t提供的备用出力。Among them, Nc is the total number of power generation companies/unit i, T is the total number of time periods, πi is the clearing electricity price of power generation companies, ui, t are 0-1 variables of the operating status of units in time period t, which represent shutdown and startup respectively , Pi,t is the output power that the unit should provide in time period t, πsw is the clearing electricity price of renewable energy power generation enterprises, PSW,t is the total output of wind farm and photovoltaic power station at time t, is the unit power generation cost of the power generation enterprise, F(Pi,t ) is the output quotation function of the unit in time period t, Si,t is the start-up cost of the unit in time period t, is the cost that the unit needs to bear when providing reserve capacity in time period t, ai , bi , and ci are the coefficients of the quotation function of uniti respectively, αi is the start-up and maintenance cost of unit i, and βi is the cold start cost, is the time that the unit has been out of operation,λi is the cooling speed of the unit, The fee to be paid for an emission unit of CO2 , is the CO2 emission of unit i corresponding to the unit output in period t, CD,i is the cost that unit i needs to pay for providing unit reserve capacity in period t, and Ri,t is the reserve output provided by unit i in period t.
所述的上层规划模型的约束条件包括：The constraints of the upper-level planning model include:
系统负荷平衡约束：System load balancing constraints:
旋转备用容量约束：Spinning reserve capacity constraints:
系统关键断面潮流约束：System key section power flow constraints:
机组爬坡约束：Crew climbing constraints:
机组出力上下限约束：Unit output upper and lower limit constraints:
机组连续启停时间约束：Unit continuous start and stop time constraints:
其中，Dt为时段t的总负荷，r为系统备用参数，Pi,max、Pi,min分别为机组的出力上、下限，Pup,i为机组每小时向上爬坡率，N为机组总数，Gl→i为机组对线路l的转移分布因子，NWS为风电及光伏发电机组总数，PWS,ws,t光伏或风电机组在时段t的中标电量，Gl→ws为风电或光伏发电机组对线路l的转移分布因子，K为节点负荷的个数，Gl→k为节点负荷k对线路l的转移分布因子，Dk,t为节点k时段t的母线负荷，为断面L的有功传输容量，Pdown,i为机组每小时向下爬坡率，Tion、Tioff分别为机组的最小连续开、停机时间，yi,t、zi,t分别为机组在时段t是否启动、关停的0-1变量，ui,tt为机组在时段tt运行状态的0-1变量。Among them, Dt is the total load of time period t, r is the system standby parameter, Pi,max and Pi,min are the upper and lower limits of the unit output respectively, Pup,i is the upward climbing rate of the unit per hour, and N is The total number of units, Gl→i is the transfer distribution factor of the unit to line l, NWS is the total number of wind power and photovoltaic power generation units, PWS,ws,t is the winning bid power of photovoltaic or wind power units in time period t, Gl→ws is the wind power Or the transfer distribution factor of photovoltaic generators to line l, K is the number of node loads, Gl→k is the transfer distribution factor of node load k to line l, Dk,t is the bus load of node k in period t, is the active power transmission capacity of the section L, Pdown,i is the downward climbing rate of the unit per hour, Tion and Tioff are the minimum continuous on and off time of the unit respectively, yi,t and zi,t are respectively is the 0-1 variable of whether the unit starts or shuts down in the period t, and ui,tt is the 0-1 variable of the unit’s operating status in the period tt.
所述的下层规划模型的目标函数为：The objective function of the described lower level programming model is:
其中，为发电企业的单位电量报价，PiG为火力发电企业的月度或年度中标电量，为可再生能源的单位电量报价，PSW为可再生能源发电企业的月度或年度中标电量，NU为参与市场竞价的电力用户的总数，为电力用户j的单位电量报价，为电力用户j的月度或年度中标电量，di为发电企业竞标电价的报价系数，为发电企业的单位发电成本。in, P i G is the unit power quotation of power generation enterprises, PiG is the monthly or annual bidding power of thermal power generation enterprises, is the unit power quotation of renewable energy, PSW is the monthly or annual winning bid power of renewable energy power generation companies, NU is the total number of power users participating in market bidding, is the unit electricity price quotation of power user j, is the monthly or annual bidding power of power user j, di is the quotation coefficient of power generation company's bidding price, is the unit power generation cost of the power generation company.
所述的下层规划模型的约束条件为：The constraints of the lower-level planning model are:
火力发电量约束：Thermal power generation constraints:
可再生能源发电量约束：Renewable energy generation constraints:
电力用户竞标电量限制约束：Constraints on electric power user's bidding power limit:
发电企业和电力用户的电量平衡约束：Power balance constraints of power generation companies and power users:
发电企业的报价系数约束：The quotation coefficient constraints of power generation enterprises:
其中，分别为火力发电企业的月度或年度中标电量上、下限，PSW，min、PSW，max分别为可再生能源发电企业的月度或年度中标电量上、下限，分别为电力用户j的月度或年度中标电量上、下限，di，min、di，max分别为发电企业的报价系数上、下限。in, are the upper and lower limits of the monthly or annual bid-winning power of thermal power generation companies, respectively, PSW, min , PSW, max are the monthly or annual bid-winning power upper and lower limits of renewable energy power generation companies, respectively, are the upper and lower limits of the monthly or annual bidding power of power user j, respectively, and di, min , di, max are the upper and lower limits of the quotation coefficients of power generation companies, respectively.
所述的步骤3)具体包括以下步骤：Described step 3) specifically comprises the following steps:
31)输入原始数据、初始出清电价及粒子群算法的动态参数，并将迭代次数k1置1；31) Input the original data, the initial clearing electricity price and the dynamic parameters of the particle swarm optimization algorithm, and set the number of iterations k1 to 1;
32)形成初始粒子群的粒子位置和粒子速度，并将种群数以及迭代次数k2置1；32) Form the particle position and particle velocity of the initial particle group, and set the population number and the number of iterations k2 to 1;
33)更新粒子的速度和位置，使初始粒子的全局最优解和个体最优解取值为一个足够大的数，并计算当前粒子的适应值，记录机组在该启停组合下的最优负荷分配和最优发电企业负利润；33) Update the speed and position of the particles, so that the global optimal solution and individual optimal solution of the initial particle are a sufficiently large number, and calculate the fitness value of the current particle, and record the optimal value of the unit under this start-stop combination. Load distribution and negative profit of the optimal power generation company;
34)对于每个粒子，将其适应度值与当前个体极值比较，若小于当前个体极值，则更新当前的个体极值为此时的适应度值；34) For each particle, compare its fitness value with the current individual extremum, if it is less than the current individual extremum, then update the current individual extremum to be the fitness value at this time;
35)判断种群数量是否达到种群总数，若已达到种群总数，则进行步骤36)，否则，令种群数加1，并返回步骤33)；35) Judging whether the number of populations has reached the total number of populations, if it has reached the total number of populations, then proceed to step 36), otherwise, add 1 to the number of populations, and return to step 33);
36)根据适应值的粒子位置，更新粒子群的速度和位置，判断此时的迭代次数k1是否达到最大迭代次数k1,max，若已达到k1,max则进行步骤37)，否则，令迭代次数k1加1、种群数置1，并返回步骤33)；36) Update the velocity and position of the particle swarm according to the particle position of the fitness value, and judge whether the iteration number k1 at this time reaches the maximum iteration number k1,max , if it has reached k1,max , proceed to step 37), otherwise, Add 1 to the number of iterations k1 , set the number of populations to 1, and return to step 33);
37)根据最优粒子位置，计算机组在此启停组合下的最优负荷分配以及最优发电企业负利润，并保存最优解相应的控制变量值，即各发电企业的单位发电成本作为下层优化初始参数；37) According to the optimal particle position, the optimal load distribution of the computer group under this start-stop combination and the negative profit of the optimal power generation enterprise, and save the corresponding control variable values of the optimal solution, that is, the unit power generation cost of each power generation enterprise as the lower layer Optimize the initial parameters;
38)采用非线性规划函数对下层模型进行优化求解，求得下层模型的最优解以及相应的各发电企业的出清电价，并将该出清电价作为已知参数回代至上层模型；38) Using the nonlinear programming function to optimize and solve the lower-level model, obtain the optimal solution of the lower-level model and the corresponding clearing electricity price of each power generation enterprise, and substitute the clearing electricity price as a known parameter to the upper-level model;
39)对上下层模型进行多次迭代优化，并判断是否满足终止条件，若满足，则结束计算并输出全局最优解；39) Perform multiple iterative optimizations on the upper and lower layer models, and judge whether the termination condition is satisfied, and if so, end the calculation and output the global optimal solution;
310)根据优化求解得到的全局最优解，包括各发电企业的最优分配电量、单位发电成本以及申报电价，最终各发电企业的中标电量、出清电价、总成本以及相应的社会福利，并以此分配运行。310) The global optimal solution obtained according to the optimization solution, including the optimal power distribution of each power generation company, the unit power generation cost and the declared power price, and finally the winning bid power of each power generation company, the cleared power price, the total cost and the corresponding social welfare, and Run with this assignment.
与现有技术相比，本发明具有以下优点：Compared with the prior art, the present invention has the following advantages:
本发明中提出的考虑可再生能源参与电力交易的基础上，基于二层规划理论，从交易角度出发，建立可再生能源参与中长期电力交易的二层规划模型，采用离散粒子群和连续粒子群相结合的混合算法以及非线性规划法对二层规划模型进行求解，得出最优规划方案，满足系统稳定性的前提，具有逻辑结构清晰、实用合理的优点。On the basis of the consideration of renewable energy participating in power trading proposed in the present invention, based on the two-level programming theory, from the perspective of trading, a two-level programming model for renewable energy participating in medium and long-term power trading is established, using discrete particle swarms and continuous particle swarms The combined hybrid algorithm and nonlinear programming method solve the two-level programming model, and obtain the optimal planning scheme, which meets the premise of system stability, and has the advantages of clear logical structure, practicality and reasonableness.
附图说明Description of drawings
图1为本发明的发明流程图。Fig. 1 is the invention flowchart of the present invention.
图2为本发明混合算法的流程图。Fig. 2 is a flow chart of the hybrid algorithm of the present invention.
图3为改进的IEEE14节点系统拓扑图。Figure 3 is a topology diagram of the improved IEEE14 node system.
图4为IEEE14节点系统不同可再生能源占比下的收益情况。Figure 4 shows the income of the IEEE14 node system under different proportions of renewable energy.
图5为改进的IEEE39节点系统拓扑图。Figure 5 is a topology diagram of the improved IEEE39 node system.
图6为IEEE39节点系统不同可再生能源占比下的收益情况。Figure 6 shows the income of the IEEE39 node system under different proportions of renewable energy.
具体实施方式Detailed ways
下面结合附图和具体实施例对本发明进行详细说明。The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.
实施例Example
如图1所示，一种基于二层规划方法的可再生能源参与中长期电力交易方法，包括以下步骤：As shown in Figure 1, a method for renewable energy to participate in medium and long-term power trading based on the two-level planning method includes the following steps:
S1根据能源市场的出清情况，拟定发电企业的申报电价；S1 According to the clearing situation of the energy market, formulate the declared electricity price of the power generation enterprise;
S2根据二层规划理论，从交易角度出发，建立可再生能源参与中长期电力交易的二层规划模型；S2 Based on the two-level planning theory, from the perspective of transactions, establish a two-level planning model for renewable energy to participate in medium and long-term power transactions;
S3采用离散粒子群和连续粒子群相结合的混合算法以及非线性规划法对二层规划模型进行求解，根据得到的最优解完成发电企业电量、电价的分配。S3 uses a hybrid algorithm combining discrete particle swarm and continuous particle swarm and nonlinear programming method to solve the two-level programming model, and completes the distribution of power generation enterprises' electricity and electricity prices according to the obtained optimal solution.
步骤S1中根据能源市场的出清情况，拟定发电企业的申报电价，其具体步骤为：In step S1, according to the clearing situation of the energy market, the declared electricity price of the power generation enterprise is drawn up, and the specific steps are as follows:
步骤S11：在中长期电力市场开启后，发电企业根据自身情况申报竞标电量和竞标电价，电力用户则根据上一阶段的用电情况申报竞标电量和竞标电价；Step S11: After the mid-to-long-term power market opens, power generation companies declare bidding power and bidding prices according to their own conditions, and power users declare bidding power and bidding prices according to the electricity consumption in the previous stage;
步骤S12：电力交易机构根据市场成员提交的信息，结合电力系统下一阶段的运行方式，构建可再生能源参与中长期电力交易的出清模型；Step S12: Based on the information submitted by market members, the power trading organization constructs a clearing model for renewable energy to participate in medium and long-term power trading in combination with the operation mode of the next stage of the power system;
步骤S13：电力交易机构经由电子系统采用合理的算法，对上一步的考虑可再生能源参与中长期电力交易的出清模型集中优化出清；Step S13: The power trading organization adopts a reasonable algorithm through the electronic system to centrally optimize the clearing model for the clearing model that considers the participation of renewable energy in medium and long-term power trading in the previous step;
步骤S14：在得到出清结果后，电力交易机构通过电力交易平台将包括系统出清电价、出清电量曲线等出清结果反馈给各市场成员；Step S14: After obtaining the clearing results, the power trading organization feeds back the clearing results including the system clearing electricity price and clearing power curve to each market member through the power trading platform;
步骤S15：各市场成员在收到出清信息后可根据自身情况调整竞标电价，并将调整后的竞标电价重新上报电力交易机构；Step S15: After receiving the clearing information, each market member can adjust the bidding price according to its own situation, and re-report the adjusted bidding price to the power trading institution;
步骤S16：电力交易机构在收到市场成员调整后的竞标电价后，重复步骤S23及之后的步骤，在电力交易关闭之前，市场成员可以多次调整申报电价以获得较优的市场出清结果；Step S16: After receiving the adjusted bidding price of market members, the power trading organization repeats step S23 and subsequent steps. Before the power trading is closed, market members can adjust the declared power price multiple times to obtain a better market clearing result;
步骤S17：在电力交易关闭前，发电企业和电力用户确认其参与交易，并签订交易合同。Step S17: Before the power transaction is closed, the power generation company and the power user confirm their participation in the transaction and sign a transaction contract.
步骤S2中根据二层规划理论，从交易角度出发，建立可再生能源参与中长期电力交易的二层规划模型，具体步骤为：In step S2, according to the two-level planning theory and from the perspective of trading, a two-level planning model for renewable energy participating in medium and long-term power trading is established. The specific steps are:
步骤S21：以发电企业的负利润最小为目标函数建立上层规划模型，上层目标函数如下：Step S21: Establish the upper-level planning model with the objective function of minimizing the negative profit of the power generation enterprise. The upper-level objective function is as follows:
其中：in:
式中：πi为发电企业i的出清电价；ui,t为机组i在时段t运行状态的0-1变量，分别代表关机和开机；Pi,t为机组i在时段t应提供的输出功率；πsw为可再生能源发电企业的出清电价；为发电企业i的单位发电成本；F(Pi,t)为机组i在时段t的出力报价函数；Si,t为机组i在时段t的启动成本；为机组i在时段t提供备用容量时需要承担的费用；为机组i在时段t的CO2排放费用；ai、bi、ci分别为机组i的报价函数的各项系数，αi为机组i的启动和检修成本；βi为冷启动成本，为机组已经停运的时间，λi为机组冷却速度，为排放单位CO2需支付的费用；为机组i在时段t对应于机组出力的CO2排放量，CD,i为机组i在时段t提供单位备用容量需支付的费用，Ri,t为机组i在时段t提供的备用出力。In the formula: πi is the clearing electricity price of power generation company i; ui,t is the 0-1 variable of the operating state of unit i in time period t, representing shutdown and startup respectively; Pi,t is the power unit i should provide in time period t output power; πsw is the clearing electricity price of renewable energy power generation enterprises; is the unit power generation cost of power generation company i; F(Pi,t ) is the output quotation function of unit i in period t; Si,t is the start-up cost of unit i in period t; The cost that unit i needs to bear when providing reserve capacity for time period t; is the CO2 emission cost of unit i in period t; ai , bi , and ci are the coefficients of the quotation function of unit i respectively, αi is the start-up and maintenance cost of unit i; βi is the cold start cost, is the time that the unit has been out of operation,λi is the cooling speed of the unit, Fees to be paid for emission units ofCO2 ; is the CO2 emission of unit i corresponding to the unit output in period t, CD,i is the cost that unit i needs to pay for providing unit reserve capacity in period t, and Ri,t is the reserve output provided by unit i in period t.
上层模型约束条件表示如下：The upper model constraints are expressed as follows:
式中，PSW,t为风电场以及光伏电站在t时刻的总出力；Dt为时段t的总负荷；r为系统备用参数；Pi,max、Pi,min分别为机组i的出力上、下限；Pup,i为机组i每小时向上爬坡率；N为机组总数；Gl→i表示机组i对线路l的转移分布因子；NWS为风电及光伏发电机组总数；PWS,ws,t光伏或风电机组在时段t的中标电量；Gl→ws表示风电或光伏发电机组对线路l的转移分布因子；Gl→k表示节点负荷k对线路l的转移分布因子；K表示节点负荷的个数；Dk,t为节点k时段t的母线负荷；表示断面L的有功传输容量；Pdown,i为机组i每小时向下爬坡率；分别为机组i的最小连续开、停机时间；yi,t、zi,t分别为机组i在时段t是否启动、关停的0-1变量；ui,tt为机组i在时段tt运行状态的0-1变量。In the formula, PSW,t is the total output of wind farm and photovoltaic power station at time t; Dt is the total load of period t; r is the system standby parameter; Pi,max and Pi,min are the output of unit i respectively Upper and lower limits; Pup,i is the upward climbing rate of unit i per hour; N is the total number of units; Gl→i indicates the transfer distribution factor of unit i to line l; NWS is the total number of wind power and photovoltaic generators; PWS ,ws,t is the bid-winning power of photovoltaic or wind turbines in time period t; Gl→ws represents the transfer distribution factor of wind power or photovoltaic generators to line l; Gl→k represents the transfer distribution factor of node load k to line l; K Indicates the number of node loads; Dk,t is the bus load of node k period t; Indicates the active power transmission capacity of section L; Pdown,i is the downward climbing rate of unit i per hour; are the minimum continuous on-off time of unit i; yi,t and zi,t are the 0-1 variables of whether unit i starts and shuts down in period t; ui,tt are the running time of unit i in period tt 0-1 variable for state.
在开展年度交易时，以年为运行周期，将T设置为8760小时，并用未来一年的负荷预测数据带入计算；在开展月度交易时，以一个月为运行周期，将T设置为720小时，并用未来一个月的负荷预测数据带入计算，若在开展月度交易时，已经事先参与过年度交易且成交，那么在进行月度交易时，机组出力上下限约束中机组出力下限需根据年度交易结果进行相应的调整。When carrying out annual transactions, take the year as the operating cycle, set T to 8760 hours, and use the load forecast data for the next year into the calculation; when carrying out monthly transactions, use one month as the operating cycle, set T to 720 hours , and use the load forecast data of the next month into the calculation. If you have participated in the annual transaction and completed the transaction before the monthly transaction, then when the monthly transaction is carried out, the lower limit of the unit output in the upper and lower limit constraints of the unit output needs to be based on the annual transaction results Adjust accordingly.
步骤S22：以社会福利最大为目标函数建立下层规划模型，下层目标函数如下：Step S22: Establish a lower-level planning model with the objective function of maximizing social welfare. The lower-level objective function is as follows:
其中：in:
式中，为发电企业i的单位电量报价；PiG为火力发电企业i的月度或年度中标电量；为可再生能源的单位电量报价；PSW为可再生能源发电企业的月度或年度中标电量；NU为参与市场竞价的电力用户的总数；为电力用户j的单位电量报价；为电力用户i的月度或年度中标电量；di为发电企业竞标电价的报价系数。In the formula, P i G is the unit power quotation of power generation company i; PiG is the monthly or annual bidding power of thermal power company i; PSW is the monthly or annual bidding power of renewable energy power generation companies; NU is the total number of power users participating in market bidding; Quotation for the unit electricity of power user j; is the monthly or annual bidding power of power user i; di is the quotation coefficient of power generation company's bidding electricity price.
下层模型约束条件表示如下：The underlying model constraints are expressed as follows:
式中，分别为火力发电企业i的月度或年度中标电量上、下限；PSW，min、PSW，max分别为可再生能源发电企业的月度或年度中标电量上、下限；分别为电力用户j的月度或年度中标电量上、下限；di，min、di，max分别为发电企业i的报价系数上、下限。本文假设中标电量的上限为0，中标电量的下限为各市场成员的申报电量。In the formula, are the upper and lower limits of the monthly or annual bid-winning power of the thermal power generation company i; PSW, min , PSSW, max are the monthly or annual bid-winning power upper and lower limits of the renewable energy power generation company respectively; are the upper and lower limits of the monthly or annual bidding power of power user j respectively; di, min , di, max are the upper and lower limits of the quotation coefficient of power generation company i respectively. This paper assumes that the upper limit of the bid-winning electricity is 0, and the lower limit of the winning bid is the declared electricity of each market member.
在开展年度交易时，上述各中标电量的上下限均为未来一年的中标电量上下限；在开展月度交易时，上述各中标电量的上下限均为未来一个月的中标电量上下限。When carrying out annual transactions, the upper and lower limits of the above-mentioned bid-winning electricity quantities are the upper and lower limits of the winning bid electricity quantities for the next year; when conducting monthly transactions, the above-mentioned upper and lower limits of the winning bid electricity quantities are the upper and lower limits of the winning bid electricity quantities for the next month.
在求解上述二层优化模型时，需要注意以下两点：发电企业i的单位发电成本在上层优化模型中作为决策变量，在下层优化模型中则作为一个已知参数，这就意味着，一旦上层模型优化得到各发电企业的单位发电成本下层模型便可通过优化求解得到各发电企业的出清电价，出清电价采用撮合出清法结算，该出清电价又可回代至上层优化模型；各火电机组的出力、风光总出力既是下层优化模型的决策变量也是上层优化模型的决策变量。When solving the above two-level optimization model, the following two points need to be paid attention to: the unit power generation cost of power generation enterprise i It is used as a decision variable in the upper-level optimization model, and as a known parameter in the lower-level optimization model, which means that once the upper-level model is optimized to obtain the unit power generation cost of each power generation enterprise The lower-level model can obtain the clearing electricity price of each power generation enterprise through optimization and solution, and the clearing electricity price is settled by matching and clearing method, and the clearing electricity price can be substituted back to the upper-level optimization model; The decision variables of the optimization model are also the decision variables of the upper optimization model.
步骤S3中采用离散粒子群和连续粒子群相结合的混合算法以及非线性规划法对二层规划模型进行求解，根据得到的最优解完成发电企业电量、电价的分配，具体步骤为：In step S3, a hybrid algorithm combining discrete particle swarm optimization and continuous particle swarm optimization and a nonlinear programming method are used to solve the two-level programming model, and the distribution of electricity and electricity prices for power generation enterprises is completed according to the obtained optimal solution. The specific steps are as follows:
步骤S31：输入原始数据、初始出清电价及粒子群算法的动态参数，并将迭代次数k1置1；Step S31: Input the original data, the initial clearing electricity price and the dynamic parameters of the particle swarm optimization algorithm, and set the number of iterations k1 to 1;
步骤S32：形成初始粒子群的粒子位置和粒子速度，并将种群数以及迭代次数k2置1；Step S32: Form the particle position and particle velocity of the initial particle group, and set the population number and the number of iterations k2 to 1;
步骤S33：更新粒子的速度和位置，使初始粒子的全局最优解和个体最优解取值为一个足够大的数，并计算当前粒子的适应值，记录机组在该启停组合下的最优负荷分配和最优发电企业负利润；Step S33: update the velocity and position of the particle, make the global optimal solution and individual optimal solution value of the initial particle a sufficiently large number, calculate the fitness value of the current particle, and record the maximum value of the unit under the start-stop combination. Optimal load distribution and negative profit of optimal power generation enterprises;
步骤S34：对于每个粒子，将其适应度值与当前个体极值比较，若小于当前个体极值，则更新当前的个体极值为此时的适应度值；Step S34: For each particle, compare its fitness value with the current individual extremum, if it is smaller than the current individual extremum, update the current individual extremum to the current fitness value;
步骤S35：判断种群数量是否达到种群总数，若已达到种群总数，则运行下一步，否则，令种群数加1，并返回步骤S3；Step S35: Determine whether the number of populations has reached the total number of populations, if it has reached the total number of populations, go to the next step, otherwise, increase the number of populations by 1, and return to step S3;
步骤S36：根据适应值的粒子位置，更新粒子群的速度和位置，判断此时的迭代次数k1是否达到最大迭代次数k1，max，若已达到k1，max则继续运行下一步，否则，令迭代次数k1加1、种群数置1，并返回步骤S3；Step S36: According to the particle position of the fitness value, update the speed and position of the particle swarm, judge whether the iteration number k1 at this time has reached the maximum iteration number k1,max , if it has reached k1,max , continue to run the next step, otherwise , add 1 to the number of iterations k1 , set the number of populations to 1, and return to step S3;
步骤S37：根据最优粒子位置，计算机组在此启停组合下的最优负荷分配以及最优发电企业负利润，并保存最优解相应的控制变量值(各发电企业的单位发电成本)作为下层优化初始参数；Step S37: According to the optimal particle position, the optimal load distribution of the computer group under this start-stop combination and the negative profit of the optimal power generation enterprise, and save the corresponding control variable value of the optimal solution (the unit power generation cost of each power generation enterprise) as The lower layer optimizes the initial parameters;
步骤S38：采用非线性规划函数对下层模型进行优化求解，求得下层模型的最优解以及相应的各发电企业的出清电价，并将该出清电价作为已知参数回代至上层模型；Step S38: Optimizing and solving the lower-level model by using a nonlinear programming function, obtaining the optimal solution of the lower-level model and the corresponding clearing electricity price of each power generation enterprise, and returning the clearing electricity price to the upper-level model as a known parameter;
步骤S39：对上下层模型进行多次迭代优化，并判断是否满足终止条件，若满足，则结束计算并输出全局最优解。Step S39: Perform multiple iterative optimizations on the upper and lower layer models, and judge whether the termination condition is satisfied, and if so, end the calculation and output the global optimal solution.
步骤S310：根据优化求解得到的发电企业最优分配电量、单位发电成本以及申报电价，结合电力用户的申报电价、申报电量，带入能量市场的出清模型中，最终求解得到各发电企业的中标电量、出清电价、总成本以及相应的社会福利。Step S310: According to the optimal distribution of power generation enterprises, unit power generation costs and declared electricity prices obtained by the optimization solution, combined with the declared electricity prices and declared electricity quantities of power users, bring them into the clearing model of the energy market, and finally solve to obtain the winning bids of each power generation company Electricity, clearing electricity price, total cost and corresponding social welfare.
实施例1Example 1
本例中，模拟的电力系统采用IEEE14节点和IEEE39节点系统。In this example, the simulated power system adopts IEEE14 node and IEEE39 node system.
IEEE14节点系统中，根据原始数据中有功负荷以及机组的分布情况，添加了2个额定容量为50MW的风电场和一个额定容量为35MW的光伏电站，风电场和光伏电站分别位于节点4、节点13以及节点9。风电场和光伏电站的总装机容量为135MW，火电机组的装机总容量为320MW，风电、光伏发电的装机容量约占总装机容量的30％。本章节算例将发电机组视为发电企业，将负荷节点视为电力用户。In the IEEE14 node system, according to the distribution of active loads and units in the original data, two wind farms with a rated capacity of 50MW and a photovoltaic power station with a rated capacity of 35MW were added. The wind farm and the photovoltaic power station are located at node 4 and node 13 respectively. and node 9. The total installed capacity of wind farms and photovoltaic power stations is 135MW, the total installed capacity of thermal power units is 320MW, and the installed capacity of wind power and photovoltaic power generation accounts for about 30% of the total installed capacity. In the calculation example in this chapter, the generator set is regarded as a power generation enterprise, and the load node is regarded as a power user.
图3为修改后的IEEE14节点系统拓扑图；IEEE14节点系统火力发电机组部分参数见表1至表3，表4为各电力用户的申报情况。在优化求解考虑可再生能源发电机组的中长期电力市场的出清结果时，对于备用及断面潮流的置信水平η2、η3、η4取95％、备用需求置信水平η’2取97％、负荷平衡置信水平η1取70％。在整理数据时，将本算例中所有可再生能源发电企业统一标记，编号6；本章算例中各发电企业的出清电价以自身的平均出清电价表示；可再生能源发电企业按常规能源发电企业的最低申报电价申报以参与市场出清，本算例假设可再生能源发电企业的单位电量申报电价为6.5$/MWh，当该区域发电企业无法提供足够的电能以满足用户需求时，可通过跨区跨省交易引入市外富余可再生能源发电企业参与发电。Figure 3 is the topology diagram of the modified IEEE14 node system; some parameters of the thermal power generating units of the IEEE14 node system are shown in Tables 1 to 3, and Table 4 shows the declaration status of each power user. When optimizing and solving the clearing results of the medium and long-term power market considering renewable energy generating units, the confidence levels η2 , η3 , and η4 for backup and cross-section power flows are taken as 95%, and the confidence level of backup demand η'2 is taken as 97%. , Load balance confidence level η1 is taken as 70%. When sorting out the data, all the renewable energy power generation enterprises in this example are uniformly marked, numbered 6; the clearing electricity price of each power generation enterprise in the example in this chapter is represented by its own average clearing electricity price; Power generation companies declare the lowest declared electricity price to participate in market clearing. In this calculation example, it is assumed that the declared electricity price per unit electricity of renewable energy power generation companies is 6.5$/MWh. Introduce surplus renewable energy power generation enterprises outside the city to participate in power generation through cross-regional and cross-provincial transactions.
表1 IEEE14节点系统支路参数Table 1 IEEE14 node system branch parameters
表2 IEEE14节点系统节点参数Table 2 IEEE14 node system node parameters
表3 IEEE14节点系统火电机组参数Table 3 IEEE14 node system thermal power unit parameters
表4 IEEE14节点系统电力用户电价申报数据Table 4. IEEE14 node system power user electricity price declaration data
为了研究投入可再生能源发电机组对中长期电力交易机制的影响，表5、6分别给出了不考虑及考虑可再生能源发电机组的中长期电力市场出清结果，表6给出了这两种不同情形下的收益情况。由表5、表6可知，由于发电成本同时考虑了运行成本、启动成本、备用成本以及碳排放成本，发电企业的单位发电成本会随着上层优化结果的变化而变化，各发电企业可根据自身发电成本调整申报电价，申报电价越低越容易成交，申报电价越高越不容易成交，且各发电企业的出清电价的高低与申报电价的高低并无太大的关联，为了获得较高的利润，各发电企业应根据自身发电成本及需求并结合市场情况选择较优的报价策略。从总体上看，考虑可再生能源发电后，电力市场的出清电价在一定程度上有所降低。由表7可知，加入可再生能源发电机组后，发电企业总利润由556417.49美元提高至688640.892美元，增长约41.42％，与此同时，社会福利也由773790美元增长至813180美元。这说明在中长期电力交易机制中考虑可再生能源发电能在保证系统可靠性的前提下，有效增加发电企业的总利润。In order to study the impact of investing in renewable energy generating units on the medium and long-term power trading mechanism, Tables 5 and 6 respectively show the clearing results of the medium and long-term electricity market without and considering renewable energy generating units, and Table 6 shows the two income in different situations. It can be seen from Table 5 and Table 6 that since the cost of power generation takes into account the operating cost, start-up cost, backup cost and carbon emission cost at the same time, the unit power generation cost of power generation enterprises will change with the change of the upper-level optimization results, and each power generation enterprise can according to its own The power generation cost adjusts the declared electricity price. The lower the declared electricity price, the easier the transaction is, and the higher the declared electricity price, the less likely it is to make a transaction. Moreover, the level of the cleared electricity price of each power generation company has little to do with the level of the declared electricity price. In order to obtain a higher Each power generation company should choose a better quotation strategy based on its own power generation cost and demand and market conditions. On the whole, after considering renewable energy power generation, the clearing electricity price in the electricity market has been reduced to a certain extent. It can be seen from Table 7 that after adding renewable energy generators, the total profit of power generation companies increased from US$556,417.49 to US$688,640.892, an increase of about 41.42%. At the same time, social benefits also increased from US$773,790 to US$813,180. This shows that considering renewable energy power generation in the medium and long-term power trading mechanism can effectively increase the total profit of power generation companies on the premise of ensuring system reliability.
表5 IEEE14节点系统不考虑可再生能源时的市场出清结果Table 5 Market clearing results of IEEE14 node system without considering renewable energy
表6 IEEE14节点系统考虑可再生能源时的市场出清结果Table 6 Market clearing results of IEEE14 node system considering renewable energy
表7 IEEE14节点系统不同情况下收益情况Table 7 Benefits of IEEE14-node systems under different conditions
为了研究不同可再生能源占比对可再生能源参与中长期电力交易的出清结果的影响，分别取可再生能源发电装机占总装机容量的比例为30％至70％，优化得到IEEE14节点系统在不同可再生能源占比下的发电企业总利润及社会福利、不同可再生能源占比下的可再生能源发电情况如图4、表8所示。由图4可知，随着可再生能源发电装机占总装机容量比例的上升，发电企业总利润及社会福利均有所增长。由表8可知，随着可再生能源发电装机容量占总电力装机容量比例的提高，可再生能源发电总量大幅提升，可再生能源出力占系统总出力的比例也随之提升。由此可见，考虑可再生能源的中长期电力交易机制可以在一定程度上促进可再生能源的消纳，且可再生能源机组装机容量占电力总装机容量的比例越高，可再生能源出力占系统总出力比例的提升幅度也越大。In order to study the influence of different proportions of renewable energy on the clearing results of renewable energy participating in medium and long-term power transactions, the proportion of renewable energy power generation installed capacity in the total installed capacity is respectively taken as 30% to 70%, and the optimized IEEE14 node system is obtained in The total profit and social welfare of power generation companies under different proportions of renewable energy, and the power generation of renewable energy under different proportions of renewable energy are shown in Figure 4 and Table 8. It can be seen from Figure 4 that with the increase in the proportion of renewable energy power generation installed capacity to the total installed capacity, the total profit and social welfare of power generation companies have increased. It can be seen from Table 8 that with the increase of the installed capacity of renewable energy in the total installed capacity of electricity, the total amount of renewable energy power generation has increased significantly, and the proportion of renewable energy output in the total output of the system has also increased. It can be seen that the medium- and long-term power trading mechanism that considers renewable energy can promote the consumption of renewable energy to a certain extent, and the higher the proportion of renewable energy installed capacity in the total installed capacity of electricity, the higher the proportion of renewable energy output in the system. The increase in the total output ratio is also greater.
表8 IEEE14节点系统不同可再生能源占比下的可再生能源发电情况Table 8 Renewable energy power generation under different proportions of renewable energy in IEEE14 node system
IEEE39系统中，根据原始数据中有功负荷以及机组的分布情况，添加了4个额定容量分别为800MW、800MW、800MW、200MW的风电场和4个额定容量分别为200MW、200MW、200MW、100MW的光伏电站，风电场和光伏电站分别位于节点4、节点8、节点16、节点20以及节点3、节5、节点15、节点21。风电场和光伏电站的总装机容量为3300MW，火电机组的装机总容量为7665MW，风电、光伏发电约占总装机容量的30％。改进后的IEEE39节点系统图如图5；IEEE39节点系统火力发电机组部分基本参数见表9至表11，各电力用户的申报情况如表12。In the IEEE39 system, according to the distribution of active loads and units in the original data, 4 wind farms with rated capacities of 800MW, 800MW, 800MW, and 200MW and 4 photovoltaic farms with rated capacities of 200MW, 200MW, 200MW, and 100MW were added. The power station, wind farm and photovoltaic power station are located at node 4, node 8, node 16, node 20 and node 3, node 5, node 15, node 21 respectively. The total installed capacity of wind farms and photovoltaic power stations is 3300MW, the total installed capacity of thermal power units is 7665MW, and wind power and photovoltaic power generation account for about 30% of the total installed capacity. The improved IEEE39 node system diagram is shown in Figure 5; the basic parameters of the IEEE39 node system thermal power generation units are shown in Tables 9 to 11, and the declaration status of each power user is shown in Table 12.
表9 IEEE39节点系统支路参数Table 9 IEEE39 node system branch parameters
表10 IEEE39节点系统节点参数Table 10 IEEE39 node system node parameters
表11 IEEE39节点系统火电机组参数Table 11 IEEE39 node system thermal power unit parameters
表12 IEEE39节点系统电力用户电价申报数据Table 12 IEEE39 node system power user electricity price declaration data
表13、14分别为不考虑及考虑可再生能源发电机组的中长期电力交易的市场出清结果。本章节在优化求解考虑可再生能源发电机组的中长期电力交易出清结果时，对于备用及断面潮流的置信水平η2、η3、η4取95％、备用需求置信水平η'2取97％、负荷平衡置信水平η1取60％。在整理数据时，将本算例中所有可再生能源发电企业统一标记，编号11；本算例假设可再生能源发电企业的单位电量申报电价为13$/MWh。由表13、表14可知，由于发电成本同时考虑了运行成本、启动成本、备用成本以及碳排放成本，发电企业的单位发电成本会随着上层优化结果的变化而变化，各发电企业可根据自身发电成本调整申报电价，申报电价越低越容易成交，申报电价越高越不容易成交，且各发电企业的出清电价的高低与申报电价的高低并无太大的关联，为了获得较高的利润，各发电企业应根据自身发电成本及需求并结合市场情况选择较优的报价策略。从总体上看，考虑可再生能源发电后，电力市场的出清电价在一定程度上有所降低。Tables 13 and 14 respectively show the market clearing results of medium and long-term power transactions without and with consideration of renewable energy generators. In this chapter, when optimizing and solving the clearing results of medium and long-term power transactions considering renewable energy generating units, the confidence levels η2 , η3 , and η4 for backup and cross-section power flows are taken as 95%, and the confidence level of backup demand η'2 is taken as 97% %, load balance confidence level η1 is taken as 60%. When collating the data, all the renewable energy power generation enterprises in this calculation example are uniformly marked, numbered 11; this calculation example assumes that the declared electricity price per unit electricity of the renewable energy power generation enterprises is 13$/MWh. It can be seen from Table 13 and Table 14 that since the cost of power generation takes into account the operating cost, start-up cost, backup cost and carbon emission cost, the unit power generation cost of a power generation company will change with the change of the upper-level optimization results. Each power generation company can according to its own The power generation cost adjusts the declared electricity price. The lower the declared electricity price, the easier the transaction is, and the higher the declared electricity price, the harder the transaction is. Moreover, the level of the cleared electricity price of each power generation company has little to do with the level of the declared electricity price. In order to obtain a higher Each power generation company should choose a better quotation strategy based on its own power generation cost and demand and market conditions. On the whole, after considering renewable energy power generation, the clearing electricity price in the electricity market has been reduced to a certain extent.
表13 IEEE39节点系统不考虑可再生能源时的市场出清结果Table 13 The market clearing results of the IEEE39 node system without considering renewable energy
表14 IEEE39节点系统考虑可再生能源时的市场出清结果Table 14 Market Clearing Results of IEEE39 Node System Considering Renewable Energy
表15为不考虑及考虑可再生能源发电机组的中长期电力市场出清结果所对应的收益情况。由表15可知，加入可再生能源发电机组后，发电企业总利润由16547049.93美元提高至23401573.23美元，增长约23.5％，与此同时，社会福利也由27476400美元增长至28059000美元。这说明在中长期电力交易机制中考虑可再生能源发电能在保证系统可靠性的前提下，有效增加发电企业的总利润，提高社会福利。Table 15 shows the income corresponding to the clearing results of the medium and long-term power market without and with consideration of renewable energy generating units. It can be seen from Table 15 that after adding renewable energy generators, the total profit of power generation companies increased from US$16547049.93 to US$23401573.23, an increase of about 23.5%. At the same time, social benefits also increased from US$27476400 to US$28059000. This shows that considering renewable energy power generation in the medium and long-term power trading mechanism can effectively increase the total profit of power generation companies and improve social welfare on the premise of ensuring system reliability.
表15 IEEE39节点系统不同情况下收益情况Table 15 Benefits of IEEE39 node systems under different conditions
图6、表16分别为可再生能源发电装机占总装机容量的比例为30％至70％时，优化得到的IEEE39节点系统的发电企业总利润及社会福利、可再生能源发电情况。由图6可知，随着可再生能源发电装机占总装机容量比例的上升，发电企业总利润及社会福利均有所增长。由表16可知，随着可再生能源发电装机容量占总电力装机容量比例的提高，可再生能源发电总量大幅提升，可再生能源出力占系统总出力的比例也随之提升。由此可见，考虑可再生能源的中长期电力交易机制可以在一定程度上促进可再生能源的消纳，且可再生能源机组装机容量占电力总装机容量的比例越高，可再生能源出力占系统总出力比例的提升幅度也越大。Figure 6 and Table 16 respectively show the total profit, social welfare, and renewable energy power generation of power generation companies in IEEE39 node systems optimized when the installed capacity of renewable energy power generation accounts for 30% to 70% of the total installed capacity. It can be seen from Figure 6 that with the increase in the proportion of renewable energy power generation installed capacity in the total installed capacity, the total profit and social welfare of power generation companies have increased. It can be seen from Table 16 that with the increase in the proportion of renewable energy installed capacity in the total installed power capacity, the total amount of renewable energy power generation has increased significantly, and the proportion of renewable energy output in the total system output has also increased. It can be seen that the medium- and long-term power trading mechanism that considers renewable energy can promote the consumption of renewable energy to a certain extent, and the higher the proportion of renewable energy installed capacity in the total installed capacity of electricity, the higher the proportion of renewable energy output in the system. The increase in the total output ratio is also greater.
表16 IEEE39节点系统不同可再生能源占比下的可再生能源发电情况Table 16 Renewable energy power generation under different proportions of renewable energy in IEEE39 node systems
| Application Number | Priority Date | Filing Date | Title |
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| CN201910501489.3ACN110310173B (en) | 2019-06-11 | 2019-06-11 | An electricity distribution method for renewable energy to participate in medium and long-term electricity trading |
| Application Number | Priority Date | Filing Date | Title |
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| CN201910501489.3ACN110310173B (en) | 2019-06-11 | 2019-06-11 | An electricity distribution method for renewable energy to participate in medium and long-term electricity trading |
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| CN110310173Atrue CN110310173A (en) | 2019-10-08 |
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| CN201910501489.3AActiveCN110310173B (en) | 2019-06-11 | 2019-06-11 | An electricity distribution method for renewable energy to participate in medium and long-term electricity trading |
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