Optimal operation management of fuel cell/wind/photovoltaic power sources connected to distribution networks

In this paper a new multiobjective modified honey bee mating optimization (MHBMO) algorithm is presented to investigate the distribution feeder reconfiguration (DFR) problem considering renewable energy sources (RESs) (photovoltaics, fuel cell and wind energy) connected to the distribution network. The objective functions of the problem to be minimized are the electrical active power losses, the voltage deviations, the total electrical energy costs and the total emissions of RESs and substations. During the optimization process, the proposed algorithm finds a set of non-dominated (Pareto) optimal solutions which are stored in an external memory called repository. Since the objective functions investigated are not the same, a fuzzy clustering algorithm is utilized to handle the size of the repository in the specified limits. Moreover, a fuzzy-based decision maker is adopted to select the ‘best’ compromised solution among the non-dominated optimal solutions of multiobjective optimization problem. In order to see the feasibility and effectiveness of the proposed algorithm, two standard distribution test systems are used as case studies.     

A modified honey bee mating optimization algorithm for multiobjective placement of renewable energy resources

Electrical generators of renewable electricity resources are quiet, clean and reliable. Optimal placement of renewable electricity generators (REGs) results in reduction of objective functions like losses, costs of electrical generation and voltage deviation. Because of recent technology developments of photovoltaic units, wind turbine and fuel cell units, only these generators are considered in this paper. This work presents a multiobjective optimization algorithm for the siting and sizing of renewable electricity generators. The objectives consist of minimization of costs, emission and losses of distributed system and optimization of voltage profile. This multiobjective optimization is solved by the Improved honey bee mating optimization (HBMO) algorithm. In the proposed algorithm, an external repository is considered to save non-dominated (Pareto) solutions found during the search process. Since the objective functions are not the same, a fuzzy clustering technique is used to control the size of the repository within the limits. This algorithm is executed on a typical 70-bus test system. Results of the case study show the proper siting and sizing of REGs are important to improve the voltage profile, reduce costs, emission and losses of distribution system. The main feature of the algorithm refers to its accuracy and calculation speed.     

Joint economic and emission dispatch in energy markets: A multiobjective mathematical programming approach

Economic load dispatch is the method of determining the most efficient, low-cost and reliable operation of a power system by dispatching the available electricity generation resources to supply the load on the system. Environmental concerns that arise due to the operation of fossil fuel fired electric generators, change the classical problem into multiobjective emission/economic dispatch (MEED) which is formulated as a constrained nonlinear multiobjective mathematical programming (MMP). The proposed MEED formulation includes emission minimization objective, AC load flow constraints and security constraints of the power system which usually are found simultaneously in real-world power systems. The proposed model has a more accurate evaluation of transmission losses obtained from the load flow equations. The MMP approach based on ?-constraint algorithm has been proposed for generating Pareto-optimal solutions of power systems MEED problem. Moreover, fuzzy decision making process is employed to extract one of the Pareto-optimal solutions as the best compromise nondominated solution. The proposed approach is simulated on the IEEE 30-bus six-generator test system and obtained results have been comprehensively compared with some of the most recently published research in the area (from the both aspects of precision and execution tome) which confirms the potential and effectiveness of the proposed approach.     

计及动态惩罚因子的风火协调优化调度

鉴于采用弃风惩罚因子在风电系统动态经济调度中存在的问题,引入动态惩罚因子,提出了一种新的电力系统动态经济调度模型——风火协调优化调度模型,并基于实际电力系统的风电场有功出力和负荷数据,在IEEE30节点系统中对计及动态惩罚因子的风火协调优化模型进行仿真模拟。结果表明,优化模型在有效降低风电场弃风量的同时,可减少系统的总成本。     

Multiobjective fuzzy dominance based bacterial foraging algorithm to solve economic emission dispatch problem

This paper proposes the bacterial foraging meta-heuristic algorithm for multiobjective optimization. In this multiobjective bacterial foraging optimization technique, the most recent bacterial locations are obtained by chemotaxis process. Next, Fuzzy dominance based sorting procedure is used here to select the Pareto optimal front (POF). To test the suitability of our proposed algorithm we have considered a highly constrained optimization problem namely economic/emission dispatch. Now-a-days environmental concern that arises due to the operation of fossil fuel fired electric generators and global warming, transforms the classical economic load dispatch problem into multiobjective environmental/economic dispatch (EED) problem. In the proposed work, we have considered the standard IEEE 30-bus six-generator test system and the results obtained by proposed algorithm are compared with the other recently reported results. Simulation results demonstrate that the proposed algorithm is a capable candidate in solving the multiobjective economic emission load dispatch problem.     

Reactive power dispatch in wind farms using particle swarm optimization technique and feasible solutions search

In this paper, an optimization method for the reactive power dispatch in wind farms (WF) is presented. Particle swarm optimization (PSO), combined with a feasible solution search (FSSPSO) is applied in order to optimize the reactive power dispatch, taking into consideration the reactive power requirement at point of common coupling (PCC), while active power losses are minimized in a WF. The reactive power requirement at PCC is included as a restriction problem and is dealt with feasible solution search. Finally an individual set point, particular for each wind turbine (WT), is found. The algorithm is tested in a WF with 12 WTs, taking into consideration different control options and different active power output levels.     

基于改进粒子群算法的多目标无功优化

提出了一种基于粒子群算法的多目标优化方法,该算法采用Pareto支配关系来更新粒子的个体最优和全局最优值,用存储池保存搜索过程中发现的非支配解;采用聚类算法裁剪非支配解,以保持解的分散性;采用动态惯性权重来平衡粒子的局部和全局搜索能力,并将该算法应用于IEEE14节点系统的多目标无功优化。     

Multiobjective optimal dispatch of microgrid based on analytic hierarchy process and quantum particle swarm optimization

Owing to the rapid development of microgrids (MGs) and growing applications of renewable energy resources, multiobjective optimal dispatch of MGs need to be studied in detail. In this study, a multiobjective optimal dispatch model is developed for a standalone MG composed of wind turbines, photovoltaics, diesel engine unit, load, and battery energy storage system. The economic cost, environmental concerns, and power supply consistency are expressed via subobjectives with varying priorities. Then, the analytic hierarchy process algorithm is employed to reasonably specify the weight coefficients of the subobjectives. The quantum particle swarm optimization algorithm is thereafter employed as a solution to achieve optimal dispatch of the MG. Finally, the validity of the proposed model and solution methodology are confirmed by case studies. This study provides reference for mathematical model of multiojective optimization of MG and can be widely used in current research field.     

干旱气候下基于风电并网的抽水蓄能电站经济调度模型

抽水蓄能电站配合风电出力有利于降低风电并网对电力系统的影响、提高电能质量、促进大规模风电并网,考虑到干旱气候可能制约抽水蓄能系统的发电和储存能力、进而影响风蓄联合系统的发电调度,以发电总可变成本的最小化为目标函数建立了基于风蓄联合系统的线性规划模型,评估了干旱气候对风蓄联合系统发电调度的影响,并利用变搜索半径优化的粒子群算法对模型进行求解。研究结果表明,干旱气候对风蓄联合系统的发电调度有较大的影响,且干旱气候可能促使系统二氧化碳排放量的增加,系统存储电能能力较强时,风电能有效促进二氧化碳减排;此外,算例分析还验证了所建模型的有效性。     

The influence of carbon dioxide trading scheme on economic dispatch of generators

This paper aims at probing into the topic of power units’ operation and dispatch based on Carbon Dioxide (CO₂) trading scheme. The trading cost of CO₂ emission is embedded into the traditional economic dispatch model, which will be solved by the New Particle Swarm Optimization (NPSO). By considering the CO₂ trading scheme, the influences of the various strategies for unit’s dispatch are simulated and analyzed in this paper. The proposed method, NPSO is developed in such a way that PSO with Constriction Factor (PSO-CF) algorithm is applied as a based level search. NPSO introduces two operators, “Random Particles” and “Fine-Tuning” into the PSO-CF algorithm to improve the drawback of searching global optimum and make the search method more efficient at the end of search. The efficiency and ability of NPSO is demonstrated by the six generating units. Simulation results indicated that reasonable solutions provide a practical and flexible framework for power sectors. They can be also used for generating alternatives and thus help decision makers to obtain the goals of minimal operation cost under their desired emission’s policies.     

Optimal hybrid PV/WT/FC sizing and distribution system reconfiguration using multi-objective artificial bee colony (MOABC) algorithm

In this paper, a novel multi-objective artificial bee colony algorithm is presented to solve the distribution system reconfiguration and hybrid (photo voltaic/wind turbine/fuel cell) energy system sizing. The purposes of the multi-objective optimization problem include the total power loss, the total electrical energy cost, and the total emission produced by hybrid energy system and the grid minimization, and the voltage stability index (VSI) of distribution system maximization. In the proposed algorithm, an external archive of non-dominated solutions is kept which is updated in each iteration. In addition, for preserving the diversity in the archive of Pareto solutions, the crowding distance operator is used. This algorithm is tested on 33 bus distribution systems and obtained non-dominated solutions are compared with the well-known NSGA-II and MOPSO methods. The solutions obtained by the MOABC algorithm have a good quality and a better diversity of the Pareto front compared with those of NSGA-II and MOPSO methods.     

基于改进径向移动算法的含风电场电力系统优化调度

为应对风电接入对电力系统稳定运行带来的影响,考虑风电高估低估成本、阀点效应、旋转备用约束和网络损耗等常需因素,建立计及风电不确定性的通用经济调度模型。为求解此模型,提出一种改进的径向移动算法(IRMO),该算法针对基本径向移动算法易陷入局部最优解的不足,一方面结合遗传算法中种群变异的思想,在迭代过程中随机对一部分粒子进行突变,改善种群多样性,使算法能够跳出局部最优;另一方面引入凹抛物线式的惯性权值非线性递减策略,以进一步增强算法中后期的搜索精度,更易找到全局最优解。最后对含风电场的电力系统进行算例分析和算法对比,验证模型的合理性以及IRMO的优越性。     

Wind energy dispatch considering environmental and economic factors

Renewable energy sources, especially wind energy, are widely applied as a mean to reach emission reduction with the increasing concern of environmental protection. Although wind generation does not produce harmful emissions, its effect on the thermal generation dispatch can actually cause an increase of emissions, especially during low or medium power demand periods of a day. A multi-objective energy dispatch that considers environment and fuel cost under large wind energy is proposed. An efficient encoding/decoding scheme that could effectively prevent unviable solutions during the application of stochastic search methods is applied; thereby dramatically improving search efficiency and solution quality. The non-linear characteristics of power generators, and their operational constraints, such as generation limitations, ramp rate limits, prohibited operating zones, and transmission loss, could be considered for practical operation. The effectiveness and feasibility of the proposed approach are demonstrated by a TAI-POWER and IEEE 30-bus test systems study. The experiment shows encouraging results, suggesting that the proposed approach is capable of providing higher quality and a wider range of Pareto-optimal solutions so that the decision makers can have a more flexible and reasonable choice.     

A novel evolutionary algorithm for dynamic economic dispatch with energy saving and emission reduction in power system integrated wind power

An optimization algorithm is proposed in this paper to solve the economic dispatch problem that includes wind farm using the Chaotic Quantum Genetic Algorithm (CQGA). In addition to the detailed models of economic dispatch introduction and their associated constraints, the wind power effect is also included in this paper. The chaotic quantum genetic algorithm used to solve the economic dispatch process and discussed with real scenarios used for the simulation tests. After comparing the proposed algorithm with several other algorithms commonly used to solve optimization problems, the results show that the proposed algorithm is able to find the optimal solution quickly and accurately (i.e. to obtain the minimum cost for power generation in the shortest time). At the end, the impact to the total cost savings for power generation after adding (or not adding) wind power generation is also discussed. The actual implementation results prove that the proposed algorithm is economical, fast and practical. They are quite valuable for further research.     

Baseload wind energy: modeling the competition between gas turbines and compressed air energy storage for supplemental generation

The economic viability of producing baseload wind energy was explored using a cost-optimization model to simulate two competing systems: wind energy supplemented by simple- and combined cycle natural gas turbines (“wind+gas”), and wind energy supplemented by compressed air energy storage (“wind+CAES”). Pure combined cycle natural gas turbines (“gas”) were used as a proxy for conventional baseload generation. Long-distance electric transmission was integral to the analysis. Given the future uncertainty in both natural gas price and greenhouse gas (GHG) emissions price, we introduced an effective fuel price, p_NGeff, being the sum of the real natural gas price and the GHG price. Under the assumption of p_NGeff=$5/GJ (lower heating value), 650 W/m² wind resource, 750 km transmission line, and a fixed 90% capacity factor, wind+CAES was the most expensive system at ¢6.0/kWh, and did not break even with the next most expensive wind+gas system until p_NGeff=$9.0/GJ. However, under real market conditions, the system with the least dispatch cost (short-run marginal cost) is dispatched first, attaining the highest capacity factor and diminishing the capacity factors of competitors, raising their total cost. We estimate that the wind+CAES system, with a greenhouse gas (GHG) emission rate that is one-fourth of that for natural gas combined cycle plants and about one-tenth of that for pulverized coal plants, has the lowest dispatch cost of the alternatives considered (lower even than for coal power plants) above a GHG emissions price of $35/tC_equiv., with good prospects for realizing a higher capacity factor and a lower total cost of energy than all the competing technologies over a wide range of effective fuel costs. This ability to compete in economic dispatch greatly boosts the market penetration potential of wind energy and suggests a substantial growth opportunity for natural gas in providing baseload power via wind+CAES, even at high natural gas prices.     

计及绿色电力证书与碳交易制度的“源-荷”协调优化调度

基于低碳电力和智能电网的背景,考虑现有的风电消纳困境,该文以绿色电力证书为基础,结合碳排放权的交易制度,同时引入需求侧高载能企业负荷的响应模型,并将虚拟电厂经济效益作为优化目标函数,建立以绿证交易为基础,结合碳交易制度和高载能需求侧响应的“源-荷”双侧互补协调优化调度模型。最后将某省份区域电网代入到该文所构建模型之中进行仿真,并采用自适应免疫疫苗算法对模型进行求解,结果表明所建立的计及绿证交易与碳交易的模型有利于促进风电消耗,降低单位发电量的碳排放。     

Wind generation modelling to help the managerial process of modern transmission systems

In this paper, a strategy is proposed in order to introduce in a realistic way wind generation into a transmission power system non sequential Monte Carlo adequacy study with economic dispatch. Thanks to the implemented solution, wind generation is consequently confronted to operational constraints related to high powered thermal units, nuclear parks or thermal machines with technical minimum value. Moreover, during each simulated system state, a DC load flow is also calculated in order to evaluate reinforcements optimizing the large scale integration of wind power production. The simulation tool modified during the present work is called Scanner© and is the property of Tractebel Engineering (Gaz de France – Suez) company. It has been here applied to an academic test system: the Roy Billinton Test System (RBTS).     

A practical multi-objective PSO algorithm for optimal operation management of distribution network with regard to fuel cell power plants

In this paper a novel Multi-objective fuzzy self adaptive hybrid particle swarm optimization (MFSAHPSO) evolutionary algorithm to solve the Multi-objective optimal operation management (MOOM) is presented. The purposes of the MOOM problem are to decrease the total electrical energy losses, the total electrical energy cost and the total pollutant emission produced by fuel cells and substation bus. Conventional algorithms used to solve the multi-objective optimization problems convert the multiple objectives into a single objective, using a vector of the user-predefined weights. In this conversion several deficiencies can be detected. For instance, the optimal solution of the algorithms depends greatly on the values of the weights and also some of the information may be lost in the conversion process and so this strategy is not expected to provide a robust solution. This paper presents a new MFSAHPSO algorithm for the MOOM problem. The proposed algorithm maintains a finite-sized repository of non-dominated solutions which gets iteratively updated in the presence of new solutions. Since the objective functions are not the same, a fuzzy clustering technique is used to control the size of the repository, within the limits. The proposed algorithm is tested on a distribution test feeder and the results demonstrate the capabilities of the proposed approach, to generate true and well-distributed Pareto-optimal non-dominated solutions of the MOOM problem.     

计及风电条件风险价值的鲁棒柔性超前调度

提出一种考虑大规模风电并网的超前优化调度方法,引入风电条件风险价值来评估风电消纳风险。建立基于鲁棒优化的柔性超前调度模型,以平衡运行成本与风电条件风险价值。根据该模型,对AGC机组的基点功率、参与因子、柔性容量进行协同优化,还可得到各风电场输出功率的可容许区域。提出一种基于大M法和分解法的求解双线性规划模型的高效算法。所提模型及算法结合鲁棒优化与随机优化的优点,在保证计算效率的同时,可避免鲁棒优化的过度保守。仿真结果验证了所提模型及算法的有效性。     

An effortless hybrid method to solve economic load dispatch problem in power systems

This paper proposes a new approach and coding scheme for solving economic dispatch problems (ED) in power systems through an effortless hybrid method (EHM). This novel coding scheme can effectively prevent futile searching and also prevents obtaining infeasible solutions through the application of stochastic search methods, consequently dramatically improves search efficiency and solution quality. The dominant constraint of an economic dispatch problem is power balance. The operational constraints, such as generation limitations, ramp rate limits, prohibited operating zones (POZ), network loss are considered for practical operation. Firstly, in the EHM procedure, the output of generator is obtained with a lambda iteration method and without considering POZ and later in a genetic based algorithm this constraint is satisfied. To demonstrate its efficiency, feasibility and fastness, the EHM algorithm was applied to solve constrained ED problems of power systems with 6 and 15 units. The simulation results obtained from the EHM were compared to those achieved from previous literature in terms of solution quality and computational efficiency. Results reveal that the superiority of this method in both aspects of financial and CPU time.