An efficient algorithm for optimal generation mix taking into account fuzziness of decision making and planning parameters

This paper presents an efficient computational algorithm for selecting the optimal generation mix under uncertain circumstances. Subjective, experiential or linguistic uncertainties are selected from among various uncertainties, i.e., we treat fuzziness in generation expansion planning. The fuzziness can be divided into: (1) the fuzziness of decision making; and (2) the fuzziness of some planning parameters, such as load growth, fuel price, and so on. Both classes of fuzziness are integrated into a fuzzy decision based on fuzzy sets theory, and then the optimal generation mix can be determined by the Fuzzy Dynamic Programming (FDP) technique. The proposed method, which is based on the dynamic programming technique, is extended by using the Bellman-Zadeh maximizing decision. In the method, each generation technology and generation capacity are selected as a stage and state, respectively. The proposed method can easily accommodate not only the fuzziness but also many constraints of generation expansion planning, such as integer solutions of unit capacities, condition of existing units, and so on. Furthermore, the arbitrary shape of membership function can be used. The effectiveness and feasibility of the proposed method are demonstrated on a typical power system model.     

A robust possibilistic mixed-integer programming method for planning municipal electric power systems

In this study, a robust possibilistic mixed-integer programming (RPMP) method is developed for planning municipal electric power systems (EPS) under uncertainty. RPMP incorporates the concept of robustness within a possibilistic mixed-integer programming framework to handle ambiguous uncertainties in the objective function and constraints. It is superior to existing fuzzy possibilistic programming method by accounting for recourse actions of deviation of objective function with imprecise parameters from its optimal value, as well as economic penalties as corrective measures of possible violation for constraints with imprecise parameters. A RPMP-based electric power system (RPMP-EPS) model is then formulated for planning EPS of the City of Shenzhen, China, while cost-effective and sustainable electricity generation schemes can be achieved through analyzing city’s electricity consumption mix, electricity balance condition, as well as energy self-sufficiency. Results demonstrate that (i) power export contracts based on national and regional energy policies bring significant effects on the municipal EPS, particularly in energy supply schemes and electricity consumption mix; (ii) although city can be basically self-sufficient in power supply if nuclear power is not enforced for export, import dependency of fuels remains extremely high, leading to the insecure fuel supply and vulnerable EPS; (iii) uncertainties have significant effects on the city’s energy source supply as well as the relevant electricity-generation scheme. The findings are helpful for formulating policies of electricity generation as well as analyzing interactions among system cost, environmental objective, and electricity supply security.     

An evaluation of the optimal generation mix for controlling carbon dioxide emissions

This paper presents an efficient computational algorithm for selecting the optimal generation mix considering CO₂ emissions. To demonstrate the effectiveness and feasibility of the proposed method, a fundamental study of the evaluation of the optimal generation mix for controlling CO₂ emissions is indicated. Furthermore, by using a parametric analysis which considers load characteristics as parameters, a general trend for the optimal generation mix which is affected by controlling CO₂ can be derived. The proposed method is based on an optimization method known as simulated annealing. In the method, solutions in a generation mix problem are equivalent to state of a physical system, and the cost of a solution is equivalent to the energy of a state. The proposed method can easily accommodate not only CO₂ emissions but also many practical constraints of generation expansion planning, such as integer solutions of unit capacities, condition of existing units, and so on. Case studies with various annual load patterns (combinations of annual load factors and the shapes of annual load duration curve) are presented and discussed. Consequently, a general trend for selecting generation technologies that should be added to a power system is derived, i.e., a useful guideline for studying generation expansion planning under controlling CO₂ emissions can be provided.     

Flexible generator maintenance scheduling based on subjective relaxation of constraints

This paper presents a fuzzy-based method for determining a flexible generator maintenance scheduling by means of subjective relaxation of constraints imposed on the maintenance scheduling problem. The constraints are divided into hard (crisp) constraint set and soft (fuzzy) constraint set according to reflecting conditions which surround power systems. The problem is formulated as a fuzzy mathematical programming problem and solved with the fuzzy branch-and-bound method using Bellman-Zadeh maximizing decision. The proposed approach provides not only a new flexible concept of planning problems in power systems, but also natural expansion of conventional approaches based on crisp set theory. The effectiveness and feasibility of the proposed approach are demonstrated on two typical power system models which consist of 15 generators and 60 generators, respectively.     

考虑网架结构和分布式电源的主动配电系统双层规划

针对主动配电系统中风光资源的波动性和负荷的随机性问题,建立了光伏发电、风力发电与负荷的多状态模型,将其不确定性转化为预想场景集。提出了以包括线路的投资、运行维护费用,分布式发电的投资、运行维护费用,网损费用及综合碳成本的系统年经济费用最小为上层规划目标函数,以分布式发电的年发电量期望值最大为下层规划目标函数的考虑网架结构和分布式电源的主动配电系统双层规划模型,引入网络连通性、辐射状、功率平衡以及无功补偿投切等约束条件,利用改进的遗传算法和原对偶内点法对上下规划模型进行求解。通过54节点的算例验证了所提模型与算法的正确性和有效性。     

考虑机组组合和网络结构优化的电网规划方法

随着风电等可再生能源发电接入规模的不断增加,电网面临的不确定性因素显著增加,对电力系统规划造成深远影响。针对目前研究中尚缺乏含可再生能源电网规划同时考虑机组组合和网络结构化的问题,以运行成本、弃可再生能源成本和切负荷成本之和最小为优化目标,综合考虑新建线路约束、网络结构优化约束和机组组合约束,并采用不同场景描述可再生能源出力和负荷需求的不同水平,建立了嵌入机组组合和网络结构优化的可再生能源接入系统电网规划模型,系统分析了电网规划方案的经济性及对可再生能源的消纳能力,为电网规划工作提供重要的辅助决策依据。通过IEEE RTS-24算例分析验证了所提方法的有效性。     

考虑发电和负荷不确定因素的输电网灵活规划

电力市场改革给输电网规划带来了新的挑战,传统的只考虑一种未来场景的输电网规划方法不能满足实际要求。对市场环境下的不确定性因素进行建模并反映到输电网规划模型中,按照线路概率性的N-1准则建立输电网灵活规划模型,采用基于蒙特卡罗仿真和遗传算法的求解方法,最终得到未来不确定性环境下投资风险最小的输电网规划方案,仿真验证了该模型的合理性和求解方法的有效性。     

含分布式变速抽水蓄能的新能源发电系统灵活性资源规划

针对含分布式变速抽水蓄能和多灵活性资源的新能源发电系统,在分析定、变速抽水蓄能运行特性差异的基础上,提出含分布式变速抽水蓄能的灵活性资源规划模型。该模型详细考虑了定、变速抽水蓄能的运行约束和库容约束,以及电池储能、需求响应、火电机组资源配置。通过松弛变速抽水蓄能的双线性约束,将所建立模型转化为以期望成本最小为目标的混合整数线性规划问题,通过两阶段随机优化方法求解得到最优的规划方案。通过与定速抽水蓄能规划方案对比,分析验证了所提分布式变速抽水蓄能规划方案的可行性以及在经济性和灵活性方面的优越性。     

满足非水可再生能源发电量占比目标的“源-网-储”协调规划

“源-网-储”协调规划是提高非水可再生能源发电量占比的关键技术之一。计及非水可再生能源和负荷的时序特征,提出了基于时序生产模拟的“源-网-储”协调规划方法。在静态规划的基础上,考虑电源、网架和储能的动态运行特征,将非水可再生能源发电量占比作为约束条件以提高模型的适用性。采用多面体集合刻画非水可再生能源和负荷的不确定性,建立满足非水可再生能源发电量占比目标的两阶段鲁棒优化模型,并引入不确定性调节参数以降低决策方案的保守性。通过强对偶理论和大M法将难以直接求解的min-max-min问题转化为双层规划问题,并采用列和约束生成算法求解得到非水可再生能源和储能的多点布局方案以及线路扩展方案。以IEEE 118节点系统为算例,分析了非水可再生能源消纳目标及不确定性对规划方案的影响,结果验证了所提方法的有效性。     

计入负荷时空转移特性的风-光-水-蓄互补系统容量配置方法

利用梯级水电的快速调节能力和抽蓄机组发电/抽水工况灵活转换的特性,在补偿风/光出力的波动性和不可控性的基础上,建立源端风-光-水-蓄系统互补模型,以增强发电系统的调节能力.同时,在计入现货电价、考虑负荷时间和空间可转移特性的基础上,建立负荷侧模型并在考虑源端风/光出力随机性、系统网络约束基础上,提出基于系统经济性、系统...     

基于机会约束的多能源枢纽电气互联综合能源系统日前经济调度

集生产、转化、储存和消费于一身的能源枢纽使电网和天然气系统的协同规划与运行成为可能,同时多种不确定性是电气互联系统调度面临的最大问题。为此构建了一种含多个能源枢纽的电气互联综合能源系统机会约束规划。首先,充分挖掘气管网的储能特性,在分别对能源枢纽、电力系统和天然气系统精细建模的基础上,考虑风电和负荷不确定性,建立了含机会约束的日前调度模型。然后,将气网潮流线性化,采用Bernstein近似将此模型转化为确定性整数混合线性凸优化问题,可利用商业软件有效求解。最后,在一个含3个能源枢纽的IEEE RST24电力系统和比利时20节点天然气系统的耦合系统中进行仿真,结果表明,气网动态特性能为综合能源系统调度提供缓冲,同时此种确定性转化方法能有效体现机会约束规划的鲁棒性。     

基于自适应模型预测控制的柔性互联配电网优化调度

针对柔性互联配电网中源荷不确定性问题,提出了一种基于改进模型预测控制的优化调度方法。建立基于模型预测控制的柔性互联配电网日内优化调度模型,采用自适应动态权重方法处理包含综合供电成本和电压偏差的多目标优化问题,在预测模型部分采用动态场景生成及K-means聚类场景削减方法对源荷预测误差进行处理,针对经典模型预测控制滚动优化部分域参数恒定问题,提出一种域参数自适应调整的滚动优化方法。通过四馈线互联的33节点系统仿真算例验证了所提优化调度方法的有效性。     

A new method of evaluating robust power system security against uncertainties

The rapid expansion of renewable energy (RE) generation is leading to severe problems related to the reliability of network operation. Power system security is threatened by increased uncertainties of RE outputs and reduced controllable resources. Therefore, it is important to evaluate the robustness of the system controllability in order to preserve system security against uncertainties of RE outputs. We refer to the robustness of the system in this context as ‘robust power system security’. This paper proposes a new formulation for evaluating the robust security region in controllable parameter space where the conventional N‐1 security criterion suffices in the presence of uncertainties. We specify the confidence interval (CI) of uncertain parameters and obtain the worst case feasible region defined by a set of security constraints, which is a very new approach in the treatment of uncertainties. We use the minimum set of static constraints in the load dispatching problem with CI of RE prediction to evaluate the security region, which is referred to as the robust static security region (RSS) in this paper. The proposed method is useful to assess the degree of difficulty of system planning and operation from the viewpoint of the balance between controllable generator capacity and RE uncertainties. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Generation expansion planning in electricity markets: A novel framework based on dynamic stochastic MPEC

This paper presents a novel framework for generation expansion planning (GEP) of restructured power systems under uncertainty in a multi-period horizon, which includes generation investment from a price maker perspective. The investment problem is modeled as a bi-level optimization problem. The first level problem includes decisions related to investment in order to maximize total profit in the planning horizon. The second level problem consists of maximizing social welfare where the power market is cleared. Rival uncertainties on offering and investment are modeled using sets of scenarios. The bi-level optimization problem is then converted to a dynamic stochastic MPEC and represented as a mixed integer linear program (MILP) after linearization. The proposed framework is examined on a typical six-bus power network, MAZANDARAN regional electric company (MREC) transmission network as an area of IRAN interconnected power system and IEEE RTS 24-bus network. Simulation results confirm that the proposed framework can be a useful tool for analyzing the behavior of investments in electricity markets.     

电源与输电线路联合规划的等值节点法

介绍了一种求解电源与输电线路联合规划的新方法.首先根据联合规划的特点构造了一新的模型,然后,结合电力系统本身结构特征,在联合规划模型中引入了等值节点,提出了一个新的求解联合规划方法,它可大大降低模型中变量的维数,为维数灾问题得到解决探索了一条有效途径.为进一步提高模型的灵活性和实用性,又将输电线路传输能力的安全储备系数应用于规划模型.初步仿真计算表明本文提出的方法是有效可行的.     

Affine Arithmetic-based DC Power Flow for Automatic Contingency Selection with Consideration of Load and Generation Uncertainties

Power systems operate under uncertainties from variations of loads and generation with time. Power flow in a power system becomes increasingly uncertain, especially when renewable energy, such as wind and photovoltaic generation, is integrated. In this article, a DC power flow approach based on affine arithmetic is proposed to quantify risks due to uncertainties. The proposed method has been applied to the automatic contingency selection under uncertainties. Numerical studies on IEEE test systems (30, 57, and 118 buses) have proved that the DC power flow approach based on affine arithmetic is a fast and reliable method for power system planning and operation when considering uncertainty. Test cases have shown that the proposed method is as fast as the conventional DC power flow while it is much more efficient than the Monte Carlo method. Thus, the DC power flow approach based on affine arithmetic serves as a new and effective decision support tool for planners and operators to cope with high levels of renewable energy penetration, electric vehicle load integration, and other uncertain scenarios.     

市场环境下满足机组成本回收约束的电源规划方法EI北大核心CSCD

为保证规划期内系统的发电容量充裕度,提出市场环境下满足机组成本回收约束的电源规划方法。基于市场价格信号和容量补偿机制推导机组成本回收的约束条件;将该约束嵌入电源规划模型中,构建电源规划与电力市场耦合的双层模型;利用Benders分解算法对模型进行求解,解决了传统上下层迭代求解方法不能保证得到全局最优解的问题,实现了全局优化求解。IEEE RTS-96系统算例结果验证了所提模型与方法的有效性。     

精细化日发电计划模型与方法

电网调度精细化管理水平的提高客观上要求日发电计划的精细化管理。针对传统日发电计划功能单一且难以实现准确的安全校核与网损管理的问题,提出了精细化日发电计划模型及优化算法。该模型以电网状态估计的网络拓扑和参数构造约束条件,决策目标兼顾电网经济性与安全裕度价值,具有应对电网运行不确定性的能力。采用交直流混合迭代优化算法,提出了交流潮流分析与有功优化的一体化决策方法,工程实用性强,有助于实现网损管理和安全校核工作的精细化管理。最后采用IEEE 30节点算例验证了算法的合理性。     

考虑运行方式优化和拓扑校正控制的参考电网优化方法

针对现有参考电网模型未涉及电网现状以及未关联电网运行策略的不足,提出了考虑运行方式优化和拓扑校正控制的参考电网优化方法,为输电网灵活规划提供重要参考。基于前瞻时间尺度内各时段负荷预测值和规划网络候选支路方案,构建以最小化发电成本和输电成本之和为目标,以系统正常运行状态以及N-1事故状态下的运行安全要求为约束条件的参考电网优化模型。对优化模型中混合整数非线性约束式进行处理,将其转换为混合整数二次规划模型,并采用混合整数二次规划法对优化模型进行求解,得到最终的优化方案。最后,通过算例分析说明了所提方法的有效性。     

基于联系数模型的电网灵活规划方法

介绍了联系数概念及其运算准则,提出了联系数大小比较的联系数模型和基于联系数模型的电网灵活规划模型,在该规划模型中采用成本效益分析法评价方案的优劣程度。用联系数处理电网规划中的各种不确定性信息,构造了电网规划的确定与不确定系统。算例证明该方法得到的电网灵活规划方案具有更好的适应性和灵活性,且方法计算简单,思路清晰,可用于求解大型电网灵活规划问题。