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含沼气能发电的农村多微网系统容量配置双层优化模型
Dual Layer Optimization Model for Capacity Configuration of Rural Multi-Microgrid System with Biogas Energy Generation
【目的】为解决农村地区新能源系统容量配置问题，响应乡村振兴与低碳要求，提高可再生能源的消纳率，提出一种基于子微网间交互且考虑沼气能发电的微网系统容量配置方法。【方法】首先对农村多微网结构进行了分析，并建立一个包含了微网间能量调度策略的沼气能发电的多微网系统模型；其次建立含沼气能发电的多微网系统容量配置与运行协同优化双层模型，上层目标为多微网的年综合成本最小与可再生能源利用率最大，下层目标为多微网系统的运行成本最低，并利用粒子群算法结合Cplex求解器进行求解；针对微网系统成本，采用Shapley值法对各微网进行公平分摊。【结果】Matlab仿真结果和算例分析表明：所提引入沼气能作为发电单元，能够增加新能源的渗透率；所提农村微网间电能交互，新能源与储能的容量配置总体均有减少，降低了年投资成本，使得多微网从外部购买电能成本降低9%，售电量也呈下降趋势；采用Shapley值法对微网成本进行分摊，各微网实际成本相较于各微网独立运行场景下分别降低6.3%、2%、4.4%。【结论】所提含沼气能发电的农村多微网系统容量配置双层优化模型，使得年综合成本降低，减少了碳排放，增加了新能源的内部消纳，同时减少农村负荷侧对电网的依赖，能够实现经济性与环保性。
[Objective] To solve the problem of new energy system capacity allocation in rural areas， respond to the requirements of rural revitalization and low carbon， and improve the rate of renewable energy consumption， a microgrid system capacity allocation method based on the interaction between sub-microgrids and considering biogas energy generation are proposed. [Methods] First， the structure of rural multi-microgrids was analyzed， and a multi-microgrid system model for biogas energy generation containing an inter-microgrid energy scheduling strategy was established. Subsequently， a two-layer model for capacity allocation and operation co-optimization of a multi-microgrid system incorporating biogas generation was established. The upper-level objective is to minimize the annual integrated cost of the multi-microgrid system while maximizing the utilization of renewable energy sources. Contrarily， the lower-level objective is to minimize the operating cost of the system. The model was solved using a particle swarm optimization algorithm combined with the CPLEX solver. To ensure fair cost allocation among microgrids， the Shapley value method was employed. [Results] Matlab simulation results and example analyses show that the proposed introduction of biogas energy as a power generation unit can increase the penetration rate of new energy. The proposed rural inter-microgrid power interaction effectively reduced the overall capacity allocation of the new energy and energy storage， thereby lowering the annual investment cost. It also decreased the cost of purchasing external power by 9%， although the amount of power sales was correspondingly reduced. The microgrid cost was apportioned using the Shapley value method， and the actual cost of each microgrid was reduced by 6.3%， 2%， and 4%， compared to the scenario of independent operation. Using the Shapley value method to apportion the costs of the microgrids， the actual costs of each microgrid were reduced by 6.3%， 2%， and 4.4%， compared with scenarios in which the microgrids were operated independently. [Conclusions] The proposed two-layer optimization model for the capacity allocation of rural multi-microgrid systems containing biogas energy generation resulted in lower annual integrated costs， reduced carbon emissions， increased internal consumption of new energy sources， and reduced dependence on the grid on the rural load side， which can achieve both economic and environmental friendliness.
农村多微网 / 沼气能 / 双层优化 / 粒子群算法 / 成本分摊
rural multi microgrid / biogas energy / double-layer optimization / particle swarm algorithm / cost sharing
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随着“双碳”目标的提出，微电网作为分布式电源利用的有效形式，受到能源管理应用领域的广泛关注。随着微电网数量增加，多微网端对端（peer to peer，P2P）能源交易有助于促进区域内能源的互联互济和清洁能源的就地消纳。在考虑微电网的碳排放特性和需求响应潜力的基础上，构建多微网P2P能源交易模型实现配电侧供需协同以及确定其最优交易策略。首先，提出了微电网运行优化模型，以运行费用最小化为目标鼓励微电网参与需求响应；其次，实现微电网内部购售电和储能充放电碳排放的量化，并计算微电网的碳排放成本；再次，基于广义纳什议价理论构建P2P能源交易模型并在模型中加入潮流约束，在保证电力系统安全稳定运行的基础上实现多微网的能源共享和收益分配。最后，基于IEEE-33节点系统进行算例分析。结果表明，考虑需求响应和碳排放费用的多微网P2P能源交易有助于促进微电网之间的能源共享和新能源消纳。
With the introduction of the “double carbon” target, microgrid is considered an effective form that integrates renewable power sources and improves energy utilization efficiency. In line with the increasing number of microgrids, peer-to-peer (P2P) energy trading among multiple microgrids is regarded as an effective solution for energy sharing and integration of distribution generation. This study proposes a P2P energy trading model that accounts for demand response and carbon emission characteristics to achieve supply and demand coordination while determining the optimal operation strategy. First, the microgrid in the model solves an economic optimization problem by participating in the demand response. Second, by considering carbon emission factors, the model offers a great opportunity to reduce operation and carbon emission costs, accounting for energy transactions and energy storage scheduling. To incentivize energy sharing among multiple microgrids, a P2P energy-trading model based on generalized Nash bargaining was developed that enables energy sharing and revenue allocation. In addition, optimal power flow constraints were incorporated into the model to enhance the security of power system operations. Finally, a simulation of the IEEE-33 system demonstrates that P2P energy trading among multiple microgrids effectively reduces operating costs and promotes the integration of renewable energy sources.
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微网系统中的分布式电源和负荷需求的随机问题促使微网储能容量决策成为一个研究的热点话题。本文提出了多时间尺度下微网系统中源-荷的随机性和预测出力偏差的不确定性的储能容量优化方法。利用该方法建立了系统能量平衡关系和鲁棒性经济协调指标，刻画出了储能系统容量优化方法和微网随机因子间的定量关系，并兼顾微网运行经济性的目标。结合分层理论建立了含分布式电源的微网储能容量的双层优化模型，并采用多目标粒子群算法对本文的优化模型进行求解。仿真结果表明，所提方法能够保证储能系统容量优化配置，同时能获得良好的经济效益。
The random problem of distributed power and load demand in the micro-grid system makes the selection of micro-grid energy storage capacity an important research topic. This paper proposes an energy storage capacity optimization method for micro-grid systems based on the randomness of source-load and the uncertainty of predicted output deviation over multiple time scales. his method is used to establish the system energy balance relationship and the robust economic coordination index, as well as to depict the quantitative relationship between the energy storage system capacity optimization method and the micro-grid stochastic factor Furthermore, the goal of the micro-grid operation economy is considered. In this paper, a two-layer optimization model of the energy storage capacity of the micro-grid with distributed power sources is established and solved using the multi-objective particle swarm optimization (MOPSO) algorithm. The simulation results demonstrate that the proposed method not only ensures the optimal configuration of the energy storage system capacity but also achieves good economic efficiency. |
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