Stochastic multiobjective self-scheduling of a power producer in joint energy and reserves markets

This paper presents a stochastic multiobjective model for self-scheduling of a power producer which participates in the day-ahead joint energy and reserves markets. The objective of a power producer is to compromise the conflicting objectives of payoff maximization and gaseous emissions minimization when committing its generation of thermal units. The proposed schedule will be used by the power producers to decide on emission quota arbitrage opportunities and for strategic bidding to the energy and reserves market. The paper analyzes a scenario-based multiobjective model in which random distributions, such as price forecasting inaccuracies as well as forced outage of generating units are modeled as scenarios tree using a combined fuzzy c-mean/Monte-Carlo simulation (FCM/MCS) method. With the above procedure the stochastic multiobjective self-scheduling problem is converted into corresponding deterministic problems. Then a multiobjective mathematical programming (MMP) approach based on ?-constraint method is implemented for each deterministic scenario. Piecewise linearized fuel and emission cost functions are applied for computational efficiency and the model is formulated as a mixed-integer programming (MIP) problem. Numerical simulations for a power producer with 21 thermal units are discussed to demonstrate the performance of the proposed approach in increasing expected payoffs by adjusting the emission quota arbitrage opportunities.     

Probabilistic reactive power procurement in hybrid electricity markets with uncertain loads

This paper presents a novel probabilistic algorithm for optimal reactive power provision in hybrid electricity markets. The proposed algorithm is a six-stage multiobjective nonlinear constrained optimization problem which takes into account load forecasting inaccuracies. Considering a set of probable forecasted loads, a three-component expected total market payment function is suggested being minimized as cost function of the first stage. Besides economic issues, expected voltage security margin, deviation from multilateral and pool based energy transactions, deviation from spinning reserve contracts, having adequate local reactive power reserve in each voltage control area of the system and transmission congestion probability are well thought out in stages 2-5 as technical aspects of the market. Finally, in the last stage, using different weighting factors to compromise between all objects, a probabilistic multiobjective function is presented to find the best reactive power market schedule. The proposed algorithm is applied on IEEE 24-bus test system. As a benchmark, Monte Carlo Simulation method is utilized to simulate the market of given period of time to evaluate results of the proposed algorithm, and satisfactory results are achieved.     

Multi-objective market clearing of electrical energy,spinning reserves and emission for wind-thermal power system

This paper proposes energy and spinning reserve market clearing mechanism for wind-thermal power system, including uncertainties in wind power generation and load demand forecasts. The impact of wind power and load demand volatility on the energy and spinning reserve market is taken into account. This paper considers reserve offers from the conventional thermal generators. The stochastic behavior of wind speed, and wind power is represented by Weibull probability density function (PDF), and the load demand uncertainty is represented by Normal PDF. This paper considers two objectives: energy and spinning reserves cost minimization, and emission minimization. The energy and spinning reserves cost minimization objective includes cost of energy provided by conventional thermal generators and wind generators, cost of reserves provided by conventional thermal generators. It also includes costs due to over-estimation and under-estimation of available wind power, and load demand. The proposed market clearing model provides a compromise solution by optimizing both the objectives simultaneously using multi-objective Strength Pareto Evolutionary Algorithm 2+ (SPEA 2+). The effectiveness of the proposed approach is established from the results on IEEE 30 bus system.     

激励性含风电竞标日前市场出清电价机制的建模

在日前市场出清电价机制中,为同时实现发电机组个体理性和激励相容以及市场出清总成本最小化的要求,应用基于VCG (Vickrey-Clarke-Groves)的机制设计理论,给出了一种含风力机组竞标的日前市场出清电价机制。建立了含风电竞标日前市场出清模型,采用抽样平均逼近法求解该市场出清模型。基于VCG机制设计理论,定义发电机组(常规/风电)获得的支付为该机组参与日前市场出清前后,日前市场出清总成本的变化量。采用某2节点系统和修改的IEEE14节点系统为例说明了该含风电竞标日前市场出清电价机制的基本特征。仿真结果表明,所提电价机制满足市场出清总成本最小化、激励相容以及个体理性等要求。     

Security-constrained transmission expansion planning: A stochastic multi-objective approach

This article presents a stochastic multi-objective optimization framework for transmission expansion planning (TEP) with steady state voltage security management, using AC optimal power flow (AC-OPF). The objectives are to minimize the sum of transmission investment costs (ICs), minimize the Expected Operation Cost (EOC), minimize the Expected Load Shedding Cost (ELSC) and maximize the Expected Loading Factor (ELF). The system load uncertainty has been considered and the corresponding scenarios are generated employing the Monte Carlo (MC) simulations. A scenario reduction technique is applied to reduce the number of scenarios. A multi-objective mathematical programming (MMP) is formulated and the ε-constraint method is used to solve the formulated problem. The N − 1 contingency analysis is also considered for the proposed TEP problem.The proposed TEP model has been applied to the well-known IEEE 24-bus Reliability Test System. The detailed results of the case study are presented and thoroughly analyzed. The obtained TEP results show the efficiency of the proposed algorithm.     

高比例新能源接入下计及工业负荷特性的电网需求响应调控策略

为挖掘需求侧调节潜力,提出一种高比例新能源接入下计及工业负荷特性的电网需求响应调控策略。设计一种基于工业负荷需求响应的滚动调度框架,通过分析不同类型工业负荷的生产特性,挖掘工业负荷的需求响应潜力;针对新能源和负荷的不确定性,提出一种结合特征损失的条件深度卷积生成对抗网络场景生成方法,为系统调控提供不同时间尺度下的典型场景集;基于生成的场景集,以系统总运行成本最小为目标,提出多场景随机规划结合随机模型预测控制方法,构建多时间尺度滚动调度优化模型,求得不同阶段工业负荷需求响应的最优策略。改进IEEE 30和IEEE 118节点系统的仿真结果验证了所提模型及策略的适用性和有效性。     

一种售电公司经济调度双层优化方法

为解决大型售电公司应对新能源接入和需求侧波动带来的随机性风险问题,文章提出了一种基于随机规划的双层优化模型:上层子问题以出清电价和出清电量为决策变量,模拟了市场电能与备用辅助服务的联合出清过程;下层子问题以售电公司的最优购电成本为目标,模拟了售电公司的经济调度行为。模型的求解过程中,通过机会约束规划将模型转换为确定性模型,再由CPLEX求解器分别求解上下层子模型,并通过上下层的迭代求得均衡解。最后,建立了不计及售电公司决策影响的两阶段模型作为对比模型,通过算例分析,验证了该模型可有效反映售电公司决策行为对出清过程的影响,并阐明了不同置信水平对售电公司决策的影响。     

一种售电公司经济调度双层优化方法

为解决大型售电公司应对新能源接入和需求侧波动带来的随机性风险问题,文章提出了一种基于随机规划的双层优化模型:上层子问题以出清电价和出清电量为决策变量,模拟了市场电能与备用辅助服务的联合出清过程;下层子问题以售电公司的最优购电成本为目标,模拟了售电公司的经济调度行为。模型的求解过程中,通过机会约束规划将模型转换为确定性模型,再由CPLEX求解器分别求解上下层子模型,并通过上下层的迭代求得均衡解。最后,建立了不计及售电公司决策影响的两阶段模型作为对比模型,通过算例分析,验证了该模型可有效反映售电公司决策行为对出清过程的影响,并阐明了不同置信水平对售电公司决策的影响。     

Power System Risk Assessment and Control in a Multiobjective Framework

Traditional online security assessment determines whether the system is secure or not, but how secure or insecure is not explicitly indicated. This paper develops probabilistic indices, risk, to assess real-time power system security level. Risk captures not only event likelihood, but also consequence. System security level associated with low voltage and overload can be optimally controlled, using the NSGA multiobjective optimization method. A security diagram is used to visualize operating conditions in a way that enables both risk-based and traditional deterministic views. An index for cascading overloads is used to evaluate the Pareto optimal solutions. This paper shows that the multiobjective approach results in less risky and less costly operating conditions, and it provides a practical algorithm for implementation. The IEEE 24-bus RTS-1996 system is analyzed to show that risk-based system security control results in lower risk, lower cost, and less exposure to cascading outages.      

Market-clearing with stochastic security-part I: formulation

The first of this two-paper series formulates a stochastic security-constrained multi-period electricity market-clearing problem with unit commitment. The stochastic security criterion accounts for a pre-selected set of random generator and line outages with known historical failure rates and involuntary load shedding as optimization variables. Unlike the classical deterministic reserve-constrained unit commitment, here the reserve services are determined by economically penalizing the operation of the market by the expected load not served. The proposed formulation is a stochastic programming problem that optimizes, concurrently with the pre-contingency social welfare, the expected operating costs associated with the deployment of the reserves following the contingencies. This stochastic programming formulation is solved in the second companion paper using mixed-integer linear programming methods. Two cases are presented: a small transmission-constrained three-bus network scheduled over a horizon of four hours and the IEEE Reliability Test System scheduled over 24 h. The impact on the resulting generation and reserve schedules of transmission constraints and generation ramp limits, of demand-side reserve, of the value of load not served, and of the constitution of the pre-selected set of contingencies are assessed.     

Comparison of security constrained economic dispatch formulations to incorporate reliability standards on demand response resources into Midwest ISO co-optimized energy and ancillary service market

The purpose of this paper is to analyze three formulations developed to facilitate participation of demand response resource Type-1 in the Midwest ISO's co-optimized energy and ancillary service market. While these three formulations appear similar on the surface, careful analysis will show that they can have different impacts on clearing and pricing outcomes. Based on this analysis, the formulation that can maintain reserve product priority and reserve clearing price order is selected and implemented in the Midwest ISO Security Constrained Economic Dispatch (SCED) and Security Constrained Unit Commitment (SCUC) market clearing processes. A 5-bus system is used to illustrate the features of each approach. This paper focuses on SCED formulations. The impact from the discussed formulations on SCUC should be similar.     

考虑虚拟电厂多类电力产品的配电侧市场出清策略

为完善电力市场运营机制,挖掘不同类别虚拟电厂各自的优势,提出了一种虚拟电厂介入的配电侧市场出清策略。文中首先对虚拟电厂参与配电侧市场的商业运营模式和组织方法进行探讨,进而提出了一种考虑多种电力市场产品耦合关系的配电侧市场联合出清模型并推导了相应定价方法,所涉及的电力市场产品包括有功功率、无功功率、旋转备用、调频容量和调频里程。中国中山25节点实际配电系统和Matpower 141节点标准配电系统的案例仿真结果表明,所提配电侧市场方法具有高求解效率和普遍适用性。     

Bi-level optimization based two-stage market clearing model considering guaranteed accommodation of renewable energy generation

Outage recovery is important for reducing the economic cost and improving the reliability of a distribution system (DS) in extreme weather and with equipment faults. Previous studies have separately considered network reconfiguration (NR) and dispatching mobile power sources (MPS) to restore the outage load. However, NR cannot deal with the scenario of an electrical island, while dispatching MPS results in a long power outage. In this paper, a resilient outage recovery method based on co-optimizing MPS and NR is proposed, where the DS and traffic network (TN) are considered simultaneously. In the DS, the switch action cost and power losses are minimized, and the access points of MPSs are changed by carrying out the NR process. In the TN, an MPS dispatching model with the objective of minimizing power outage time, routing and power generation cost is developed to optimize the MPSs’ schedule. A solution algorithm based on iteration and relaxation methods is proposed to simplify the solving process and obtain the optimal recovery strategy. Finally, numerical case studies on the IEEE 33 and 119-bus systems validate the proposed resilient outage recovery method. It is shown that the access point of MPS can be changed by NR to decrease the power outage time and dispatching cost of MPS. The results also show that the system operation cost can be reduced by considering power losses in the objective function.     

Bi-level optimization based two-stage market clearing model considering guaranteed accommodation of renewable energy generation

The existing electricity market mechanisms designed to promote the consumption of renewable energy generation complicate network participation in market transactions owing to an unfair market competition environment, where the low cost renewable energy generation is not reflected in the high bidding price of high cost conventional energy generation. This study addresses this issue by proposing a bi-level optimization based two-stage market clearing model that considers the bidding strategies of market players, and guarantees the accommodation of renewable energy generation. The first stage implements a dual-market clearing mechanism that includes a unified market for trading the power generations of both renewable energy and conventional energy units, and a subsidy market reserved exclusively for conventional generation units. A re-adjustment clearing mechanism is then proposed in the second stage to accommodate the power generation of remaining renewable energy units after first stage energy allocations. Each stage of the proposed model is further described as a bi-level market equilibrium problem and is solved using a co-evolutionary algorithm. Finally, numerical results involving an improved IEEE 39-bus system demonstrate that the proposed two-stage model meets the basic requirements of incentive compatibility and individual rationality. It can facilitate the rational allocation of resources, promote the economical operation of electric power grids, and enhance social welfare.     

A method for reserve clearing in disaggregated model considering lost opportunity cost

In this paper, a new formulation for clearing reserve market in a deregulated environment with separated energy and reserve market is introduced. In the proposed method, reserve market is cleared such that the costs associated with capacity reservation, producing energy in real-time, opportunity cost of those units which are accepted in the energy market and backed down from the accepted values to participate in the reserve market are minimized. This optimization problem is formulated and solved using linear programming method. Finally, the proposed method is applied to a six units test system to examine the applicability and effectiveness of the proposed method.     

Siting and Sizing of Distributed Generation and Shunt Capacitor Banks in Radial Distribution System Using Constriction Factor Particle Swarm Optimization

Abstract

This article proffers an advanced solution to allocate the distributed generation (DG) and shunt capacitor (SC) banks optimally in radial distribution system (RDS) to alleviate the power losses, revamp the voltage profile, inflate the voltage stability index, shrink the total voltage deviation, and acquire more energy savings. In the framework of finding the optimal site and size of DGs and SC banks, Particle swarm optimization (PSO) technique is modified with constriction factor and the same is aimed to apply in IEEE 33-bus system and practical Brazil 136-bus RDS. Different case studies are carried out for multiobjective problems while considering equality and inequality constraints. A comparative analysis with PSO and modified PSO is presented to project the precedence of the adopted algorithm. In addition, the obtained results are also compared to the recently surfaced few state-of-the-art algorithms to present the optimizing capability of the implemented algorithm.     

计及广义负荷不确定性和激励型需求响应的电力现货市场竞价方法

以国内目前电力现货市场运营机制为基础,提出一种计及分布式发用电单元功率预测不确定性以及激励型需求响应资源功率调节能力的广义负荷参与日前电力现货市场竞价的方法,并构建相应的市场出清模型。统计得到广义负荷净功率预测误差的条件概率分布,推导广义负荷日内购电期望和需求响应补偿的成本函数,揭示价格引导模式下广义负荷激励型需求响应的响应量-申报电价-申报电量之间的关系,使广义负荷原本多元、随机的报价曲线等效转化为符合市场统一规范的一元确定性报价曲线。采用不均匀最优分段技术将连续型报价曲线转化为规范的离散型阶梯曲线,并将其代入动态市场出清模型中进行竞价出清。仿真结果验证了出清电价对分布式新能源发电和负荷用电平衡情况的刻画能力。     

电力市场中弹性运行备用研究

备用市场组织方式关系到备用市场稳定运行.传统顺序出清方式中,备用出清的先后顺序对出清结果影响重大,市场的公平性不容易保障.该文在弹性备用框架下,提出各类备用联合出清的新型交易形式.引入可靠性指标LOLP（电力不足概率）,将备用容量报价和电量报价结合起来作为综合报价指标,并分析了按报价付费模式和统一出清价模式在备用市场中的应用.在此基础上,结合可靠性评估理论,建立了同时出清10分钟备用和30分钟备用的弹性备用综合模型.文章采用启发式算法求解,简便实用.标准测试系统RTS96的仿真计算结果表明该模型的有效性     

考虑负荷量测和光伏不确定性的主动配电网鲁棒电压控制

由于量测不足以及光伏发电出力的不确定性,主动配电网中的负荷和光伏发电出力难以准确估计,不考虑这些误差的电压优化控制策略无法保证系统的安全性。针对辐射状运行的配电网,文中首先介绍了基于支路潮流形式的主动配电网确定性电压优化控制模型。在此基础上,建立了考虑光伏发电和负荷不确定性的鲁棒电压控制模型。为了减小鲁棒电压控制模型的计算量,采用同步回代削减法对生成的大规模原始场景进行削减,可保证削减后场景具有很好的近似性。采用修改的IEEE 33节点算例系统进行算例分析,验证了该方法的鲁棒性和有效性。     

Probabilistic and Multi-Objective Placement of D-STATCOM in Distribution Systems Considering Load Uncertainty

Advanced technologies such as distribution static compensator (D-STATCOM) are broadly deployed in distribution systems (DSs) to tackle different issues. Successful deployment of such technologies requires their adaption to stochastic nature of DS. This paper proposes a new method for allocation of D-STATCOM in DSs considering load uncertainty. Monte Carlo simulation (MCS) technique is utilized to cope with uncertain attribute of loads. To do so, output of MCS is considered as a deterministic case which is solved by backward-forward sweep power flow approach. The objective functions of the proposed model include total voltage deviation, total active losses, and voltage stability metrics. These metrics are combined in a multi-objective optimization formulation and solved by non-dominated sorting genetic algorithm-II (NSGA-II). Lastly, the best compromise solution is determined by Pareto front technique taking advantage of fuzzy decision making (FDM) engine. Comprehensive simulation studies are carried out on IEEE 33-bus standard distribution system as well as on Portuguese 94-bus real-world distribution system. Simulation studies confirm the accuracy of the proposed method in determining proper location of D-STATCOM in DSs by fulfilling the objectives.