Multiobjective clearing of reactive power market in deregulated power systems

This paper presents a day-ahead reactive power market which is cleared in the form of multiobjective context. Total payment function (TPF) of generators, representing the payment paid to the generators for their reactive power compensation, is considered as the main objective function of reactive power market. Besides that, voltage security margin, overload index, and also voltage drop index are the other objective functions of the optimal power flow (OPF) problem to clear the reactive power market. A Multiobjective Mathematical Programming (MMP) formulation is implemented to solve the problem of reactive power market clearing using a fuzzy approach to choose the best compromise solution according to the specific preference among various non-dominated (pareto optimal) solutions. The effectiveness of the proposed method is examined based on the IEEE 24-bus reliability test system (IEEE 24-bus RTS).     

基于带权极小模理想点法的多目标特高压穿越无功规划

特高压交流线路充电功率大,导致其与下级电网存在较大的穿越无功,影响系统经济性与安全稳定性。对此,从技术性与经济性两个维度,构建了以特高压穿越无功最小、投资成本最小为目标的多目标特高压穿越无功规划模型。针对模型特点,采用带权极小模理想点法,将多目标规划问题转化为单目标规划问题,避免了求解多目标规划问题复杂的Pareto前端曲面,简化了问题求解复杂度。最后,以某实际区域电网为例,仿真结果验证了所提模型及其求解方法的有效性。研究成果可为其他区域电网特高压穿越无功规划提供参考。     

An efficient algorithm for multi-objective optimal operation management of distribution network considering fuel cell power plants

This paper presents an interactive fuzzy satisfying method based on Hybrid Modified Honey Bee Mating Optimization (HMHBMO). Its purpose is to solve the Multi-objective Optimal Operation Management (MOOM) problem which can be affected by Fuel cell power plants (FCPPs). Minimizing total electrical energy losses, total electrical energy cost, total pollutant emission produced by sources and deviation of bus voltages are the objective functions in this method. A new interactive fuzzy satisfying method is presented to solve the multi-objective problem by assuming that the decision-maker (DM) has fuzzy targets for each of the objective functions. Through the interaction with the DM, the fuzzy goals are quantified by eliciting the corresponding membership functions. Considering the current solution, the DM updates the reference membership values until the best solution can be obtain. The MOOM problem is modeled as a mixed integer nonlinear programming problem. Therefore, evolutionary methods can be used to solve this problem since they are independence of objective function’s type and constraints. Recently researchers have presented a new evolutionary method called Honey Bee Mating Optimizations (HBMO) algorithm. Original HBMO often converges to local optima and this is a disadvantage of this method. In order to avoid this shortcoming we propose a new method. This method improves the mating process and also combines the modified HBMO with a Chaotic Local Search (CLS). Numerical results on a distribution test system have been presented to illustrate the performance and applicability of the proposed method.     

A comprehensive method for optimal power management and design of hybrid RES-based autonomous energy systems

The power management strategy (PMS) plays an important role in the optimum design and efficient utilization of hybrid energy systems. The power available from hybrid systems and the overall lifetime of system components are highly affected by PMS. This paper presents a novel method for the determination of the optimum PMS of hybrid energy systems including various generators and storage units. The PMS optimization is integrated with the sizing procedure of the hybrid system. The method is tested on a system with several widely used generators in off-grid systems, including wind turbines, PV panels, fuel cells, electrolyzers, hydrogen tanks, batteries, and diesel generators. The aim of the optimization problem is to simultaneously minimize the overall cost of the system, unmet load, and fuel emission considering the uncertainties associated with renewable energy sources (RES). These uncertainties are modeled by using various possible scenarios for wind speed and solar irradiation based on Weibull and Beta probability distribution functions (PDF), respectively. The differential evolution algorithm (DEA) accompanied with fuzzy technique is used to handle the mixed-integer nonlinear multi-objective optimization problem. The optimum solution, including design parameters of system components and the monthly PMS parameters adapting climatic changes during a year, are obtained. Considering operating limitations of system devices, the parameters characterize the priority and share of each storage component for serving the deficit energy or storing surplus energy both resulted from the mismatch of power between load and generation. In order to have efficient power exploitation from RES, the optimum monthly tilt angles of PV panels and the optimum tower height for wind turbines are calculated. Numerical results are compared with the results of optimal sizing assuming pre-defined PMS without using the proposed power management optimization method. The comparative results present the efficacy and capability of the proposed method for hybrid energy systems.     

Impact of heat,power and hydrogen generation on optimal placement and operation of fuel cell power plants

In this paper, a stochastic model is proposed for planning the location and operation of Fuel Cell Power Plants (FCPPs) as Combined Heat, power, and Hydrogen (CHPH) units. Total cost, emissions of FCPPs and substation, and voltage deviation are the objective functions to be minimized. Location and operation of FCPPs as CHPH are considered in this paper while their investment cost is not taken into account. In the proposed model, indeterminacy refers to electrical and thermal loads forecasting, pressure of oxygen and hydrogen, and the nominal temperature of FCPPs. In this method, scenarios are produced using roulette wheel mechanism and probability distribution function of input random variables. Using this method, the probabilistic problem is considered to be distributed as some scenarios and consequently probabilistic problem is considered as combination of some deterministic problems. Considering the nature of objective functions, the problem of locating and operating FCPPs as CHPH is considered as a mixed integer nonlinear problem. A Self Adaptive Charged System Search (SACSS) algorithm is employed for determining the best Pareto optimal set. Furthermore, a set of non-dominated solutions is saved in repository during simulation procedure. A 69-bus distributed system is used for verifying the beneficiary proposed method.     

Particle swarm optimization for AC-coupling stand alone hybrid power systems

The main purpose of this study was to present a technique on how to optimize the configuration of a typical AC-coupling stand alone hybrid power system (SAHPS). The design was posed as an optimization problem whose solution allowed obtaining the configuration of the SAHPS that minimized the total cost through the useful life of the system. To verify the system component models, an existing PV/wind/diesel hybrid power system at Chik Island, Thailand, was selected as a reference system, and the in situ monitoring results were compared with the simulation results. The minimization of the objective function was evaluated using TRNSYS 16 in assistance with GenOpt (optimization program). The result showed that the overall best cost reduction has been achieved by the particle swarm optimization (PSO) with constriction coefficient algorithm. This method requires just a few seconds to give the best results (where the number of generations in the algorithm is 46). It is thus believed that the present method would decrease the time required by design engineers to find the SAHPS optimum solution.     

Probabilistic multi-objective optimal power flow considering correlated wind power and load uncertainties

Increasing penetration of wind power in power systems causes difficulties in system planning due to the uncertainty and non dispatchability of the wind power. The important issue, in addition to uncertain nature of the wind speed, is that the wind speeds in neighbor locations are not independent and are in contrast, highly correlated. For accurate planning, it is necessary to consider this correlation in optimization planning of the power system. With respect to this point, this paper presents a probabilistic multi-objective optimal power flow (MO-OPF) considering the correlation in wind speed and the load. This paper utilizes a point estimate method (PEM) which uses Nataf transformation. In reality, the joint probability density function (PDF) of wind speed related to different places is not available but marginal PDF and the correlation matrix is available in most cases, which satisfy the service condition of Nataf transformation. In this paper biogeography based optimization (BBO) algorithm, which is a powerful optimization algorithm in solving problems including both continuous and discrete variables, is utilized in order to solve probabilistic MO-OPF problem. In order to demonstrate performance of the method, IEEE 30-bus standard test case with integration of two wind farms is examined. Then the obtained results are compared with the Monte Carlo simulation (MCS) results. The comparison indicates high accuracy of the proposed method.     

Probabilistic multiple distribution static compensator placement and sizing based on the two-point estimate method

This paper deals with the stochastic placement and sizing of the distribution static compensator in the distribution systems considering uncertainty. The proposed stochastic framework utilises the point estimate method to consider the forecasting uncertainty of the active and reactive loads in the load flow equations. The objective functions to be investigated are total active power losses and the voltage profile simultaneously. In order to reach a proper compromise to satisfy all the objective functions, an interactive fuzzy satisfying approach is employed. Also, since the problem investigated is a type of discrete, nonlinear, multi-objective optimisation problem, a new modified optimisation technique is proposed to escape from the local optima as well as the premature convergence. Finally, the satisfying performance of the proposed method is examined on the 86-bus IEEE distribution system.     

基于风-光-蓄-火联合发电系统的多目标优化调度

针对风电、光伏出力的随机性、间歇性和波动性而导致其在大规模接入电网时对电网发电计划制定和调度产生的影响,提出了含风-光-蓄-火联合发电系统的多目标优化调度模型。利用抽水蓄能的抽蓄特性,将风电和光伏出力进行时空平移,使风-光-蓄联合出力转变为稳定可调度电源,具备削峰填谷的功能,与火电机组共同参与系统优化调度。以风-光-蓄联合出力最大、广义负荷波动最小和火电机组运行成本最小作为目标函数,建立多目标优化调度模型,通过多目标处理策略,使目标函数简化为2个,以降低问题维数;在求解阶段,利用分层求解思想,将模型划分为两层,分别采用混合整数规划方法和机组组合优化方法进行求解。10机测试系统仿真结果表明：所建模型可以提高风能和太阳能的利用率,缓解火电机组的调峰压力,大幅降低风电反调峰特性对电网的影响,从而保证电力系统安全、稳定、经济运行。     

A new technique for solving the multi-objective optimization problem using hybrid approach

Energy efficiency, which consists of using less energy or improving the level of service to energy consumers, refers to an effective way to provide overall energy. But its increasing pressure on the energy sector to control greenhouse gases and to reduce CO₂ emissions forced the power system operators to consider the emission problem as a consequential matter besides the economic problems. The economic power dispatch problem has, therefore, become a multi-objective optimization problem. Fuel cost, pollutant emissions, and system loss should be minimized simultaneously while satisfying certain system constraints. To achieve a good design with different solutions in a multi-objective optimization problem, fuel cost and pollutant emissions are converted into single optimization problem by introducing penalty factor. Now the power dispatch is formulated into a bi-objective optimization problem, two objectives with two algorithms, firefly algorithm for optimization the fuel cost, pollutant emissions and the real genetic algorithm for minimization of the transmission losses. In this paper the new approach (firefly algorithm-real genetic algorithm, FFA-RGA) has been applied to the standard IEEE 30-bus 6-generator. The effectiveness of the proposed approach is demonstrated by comparing its performance with other evolutionary multi-objective optimization algorithms. Simulation results show the validity and feasibility of the proposed method.     

A novel optimization model for combined wind power accommodation and electric boiler with thermal storage

As the installed capacity of wind power continues to increase, the problem of curtailed wind power is becoming serious in China, especially in the northern region during the winter heating season. To solve the problem of wind‐heat conflict during the heating period in the Three North area, an electric boiler with thermal storage (EBTS) is installed at the end of the grid where wind power is difficult to accommodate and using curtailed wind power to supply heat promotes local accommodation. In this paper, a multi‐objective optimization model of wind power accommodation based on the wind power–EBTS system for heating is established. The goals of maximizing wind power accommodation, minimizing the number of times EBTS must be adjusted, and minimizing operating costs are presented, and a bi‐level optimization scheme is designed. An improved multi‐objective particle swarm optimization algorithm is used to solve these functions, and an optimal compromise solution from the generated Pareto solution set is filtered using the fuzzy membership method. Based on actual data from a demonstration project in China's Jilin Province, the simulation results verify that this method can effectively reduce operating costs and improve wind power accommodation.     

Multi objective optimization of a tri-reforming process with the maximization of H2 production and minimization of CO2 emission & power loss

In this study, the process optimization of a tri-reformer reactor is conducted for the synthesis of hydrogen gas from natural gas using multi-objective optimization (MOO) approach. Specifically, four MOO problems are solved using three objective functions, namely maximization of H₂, minimization of CO₂, and minimization of power loss. It should be noticed that the power loss is an important economic factor due the large pressure drop and flowrates in packed bed reactors. However, it has not been used as an objective function for the optimization based design and/or operation of fixed bed reactor for reforming process to the best of authors’ knowledge. Three of the four MOO problems are 2-objective in nature with all the permutation and combination of the three objectives. The fourth MOO problem is solved considering all the three objectives, simultaneously. For all the MOO problems, feed conditions of O₂, H₂O, and Temperature are considered as the optimization variables. The results obtained with 3 objective functions are observed to be superior to the ones obtained from 2 objective problems.     

A modified shuffle frog leaping algorithm for multi-objective optimal power flow

This paper presents an efficient and reliable evolutionary-based approach to solve the Optimal Power Flow (OPF) problem by considering the emission issue. The OPF problem has been widely used in power system operation and planning for determining electricity prices. Therefore, the conventional optimal power flow cannot meet the environmental protection requirements, because it only considers generation cost minimization. The multi-objective optimal power flow considers economical and emission issues. By adding the emission objective in the optimal power flow problem, this problem become more complicated than before and it needs to be solved with an accurate algorithm. This paper proposes an algorithm based on the Shuffle Frog Leaping Algorithm (SLFA) to solve the multi-objective OPF problem. Furthermore, this paper presents a modified SLFA called MSLFA algorithm which profits from a mutation in order to reduce the processing time and improve the quality of solutions, particularly to avoid being trapped in local optima. The IEEE 30-bus test system is presented to illustrate the application of the proposed 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.     

基于风电机组无功裕度预测的风电场无功分层控制策略

针对风电电压波动的问题,文章基于风电机组无功裕度预测,提出了一种风电场无功分层控制策略。该策略首先以并网点电压偏差和线路有功损耗最小为目标,使用二次规划算法在线实时求解最优并网电压,进而求解风电场无功参考值;其次,采用EWT-LSSVM预测算法进行风电功率预测,并提出预测功率校正方法实时修正预测功率,精确求解风电机组的无功裕度预测值;最后,以风电机组的出口电压波动最小和预测无功裕度最大为无功分配依据,实现风电场的无功电压闭环控制。仿真结果表明,所提控制策略能够提高风电功率预测的精确性和时效性,降低了风电机组出口电压波动性,同时为风电场预留出充足的无功裕度。     

Tuned reactive power dispatch through modified differential evolution technique

This paper explores the capability of modified differential evolution (MDE) technique for solving the reactive power dispatch (RPD) problem. The proposed method is based on the basic differential evolution (DE) technique with a few modifications made into it. DE is one of the strongest optimization techniques though it suffers from the problem of slow convergence while global minima appear. The proposed modifications are tried to resolve the problem. The RPD problem mainly defines loss minimization with stable voltage profile. To solve the RPD problem, the generator bus voltage, transformer tap setting and shunt capacitor placements are controlled by the MDE approach. In this paper, IEEE 14-bus and IEEE 30-bus systems are chosen for MDE implementation. The applied modification show much improved result in comparison to normal DE technique. Comparative study with other soft-computing technique including DE validates the effectiveness of the proposed method.     

Two-stage ADMM-based distributed optimal reactive power control method for wind farms considering wake effects

Since the connection of small-scale wind farms to distribution networks, power grid voltage stability has been reduced with increasing wind penetration in recent years, owing to the variable reactive power consumption of wind generators. In this study, a two-stage reactive power optimization method based on the alternating direction method of multipliers (ADMM) algorithm is proposed for achieving optimal reactive power dispatch in wind farm-integrated distribution systems. Unlike existing optimal reactive power control methods, the proposed method enables distributed reactive power flow optimization with a two-stage optimization structure. Furthermore, under the partition concept, the consensus protocol is not needed to solve the optimization problems. In this method, the influence of the wake effect of each wind turbine is also considered in the control design. Simulation results for a mid-voltage distribution system based on MATLAB verified the effectiveness of the proposed method.     

Probabilistic approach to multi-objective Volt/Var control of distribution system considering hybrid fuel cell and wind energy sources using Improved Shuffled Frog Leaping Algorithm

Deregulation and restructuring in power systems, the ever-increasing demand for electricity, and concerns about the environment are the major driving forces for using Renewable Energy Sources (RES). Recently, Wind Farms (WFs) and Fuel Cell Power Plants (FCPPs) have gained great interest by Distribution Companies (DisCos) as the most common RES. In fact, the connection of enormous RES to existing distribution networks has changed the operation of distribution systems. It also affects the Volt/Var control problem, which is one of the most important schemes in distribution networks. Due to the intermittent characteristics of WFs, distribution systems should be analyzed using probabilistic approaches rather than deterministic ones. Therefore, this paper presents a new algorithm for the multi-objective probabilistic Volt/Var control problem in distribution systems including RES. In this regard, a probabilistic load flow based on Point Estimate Method (PEM) is used to consider the effect of uncertainty in electrical power production of WFs as well as load demands. The objective functions, which are investigated here, are the total cost of power generated by WFs, FCPPs and the grid; the total electrical energy losses and the total emission produced by WFs, FCPPs and DisCos. Moreover, a new optimization algorithm based on Improved Shuffled Frog Leaping Algorithm (ISFLA) is proposed to determine the best operating point for the active and reactive power generated by WFs and FCPPs, reactive power values of capacitors, and transformers’ tap positions for the next day. Using the fuzzy optimization method and max-min operator, DisCos can find solutions for different objective functions, which are optimal from economical, operational and environmental perspectives. Finally, a practical 85-bus distribution test system is used to investigate the feasibility and effectiveness of the proposed method.     

Estimating future balancing power requirements in wind–PV power system

This paper presents a general model—based on the Monte Carlo simulation—for the estimation of power system uncertainties and associated reserve and balancing power requirements. The proposed model comprises wind, PV and load uncertainty, together with wind and PV production simulation. In the first stage of the model, wind speed and solar irradiation are simulated, based on the plant disposition and relevant data. The second stage of the model consists of wind speed, PV power and load forecast error simulation, based on the associated statistical parameters. Finally, both wind and PV forecast error are combined with the load forecast error, resulting in the net uncertainty. This net uncertainty, aggregated on a yearly level, presents a dominant component in balancing power requirements. Proposed model presents an efficient solution in planning phase when the actual data on wind and PV production is unavailable.     

基于帝国竞争算法考虑运行风险的电力系统随机无功备用优化

针对目前电力系统中的随机无功备用优化不能控制系统总无功备用风险,从而导致对系统的安全水平评估不准确的问题,首先建立考虑目标函数置信水平的随机无功备用优化模型,采用Nataf变换重构生成风速样本,然后采用蒙特卡洛法将原问题转化为多次的确定型优化运算,最后采用帝国竞争算法对问题进行求解。算例分析结果表明,相比于传统基于期望值目标函数的随机无功备用优化,所提方法可有效控制目标函数的风险;其中帝国竞争算法的采用是该算法效率提升的关键因素。