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.     

A practical algorithm for optimal operation management of distribution network including fuel cell power plants

Fuel cell power plants (FCPPs) have been taken into a great deal of consideration in recent years. The continuing growth of the power demand together with environmental constraints is increasing interest to use FCPPs in power system. Since FCPPs are usually connected to distribution network, the effect of FCPPs on distribution network is more than other sections of power system. One of the most important issues in distribution networks is optimal operation management (OOM) which can be affected by FCPPs. This paper proposes a new approach for optimal operation management of distribution networks including FCCPs. In the article, we consider the total electrical energy losses, the total electrical energy cost and the total emission as the objective functions which should be minimized. Whereas the optimal operation in distribution networks has a nonlinear mixed integer optimization problem, the optimal solution could be obtained through an evolutionary method. We use a new evolutionary algorithm based on Fuzzy Adaptive Particle Swarm Optimization (FAPSO) to solve the optimal operation problem and compare this method with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Ant Colony Optimization (ACO) and Tabu Search (TS) over two distribution test feeders.     

Probabilistic hydrogen,thermal and electrical management of PEM-fuel cell power plants in distribution networks

This paper presents a Stochastic Multi-objective Optimal Operation Management (SMOOM) framework of distribution networks in presence of PEM-Fuel Cell Power Plants (FCPPs) and boilers. Operational costs, thermal recovery, power trade with grid and hydrogen management strategies are considered in this model. Furthermore, four objective functions has been considered as criteria for SMOOM, i.e. electrical energy losses, voltages deviations from their nominal values, total emissions emitted by CHP systems and grids, and total operational costs of CHP systems, as well as electrical energy cost of grids. A 2m + 1 Point Estimated Method is used to cope with the uncertain variables i.e. electrical and thermal loads, gas price of FCPPs consumption, fuel cost of residential loads, purchasing and selling tariff of electricity, hydrogen price, operation temperature of fuel cell stack, and the pressures of hydrogen and oxygen of anode and cathode, respectively. A new multi-objective Modified Firefly Algorithm (MFA) is implemented for minimizing the objective functions while the operational constraints are satisfied. Finally, a 69-bus distribution network is utilized to examine the performance of the proposed strategy regarding the rest.     

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.     

Voltage stability constrained multi-objective optimal reactive power dispatch under load and wind power uncertainties: A stochastic approach

Optimal reactive power dispatch (ORPD) problem is an important problem in the operation of power systems. It is a nonlinear and mixed integer programming problem, which determines optimal values for control parameters of reactive power producers to optimize specific objective functions while satisfying several technical constraints. In this paper, stochastic multi-objective ORPD (SMO-ORPD) problem is studied in a wind integrated power system considering the loads and wind power generation uncertainties. The proposed multi objective optimization problem is solved using ε-constraint method, and fuzzy satisfying approach is employed to select the best compromise solution. Two different objective functions are considered as follow: 1) minimization of the active power losses and 2) minimization of the voltage stability index (named L-index). In this paper VAR compensation devices are modeled as discrete variables. Moreover, to evaluate the performance of the proposed method for solution of multi-objective problem, the obtained results for deterministic case (DMO-ORPD), are compared with the available methods in literature. The proposed method is examined on the IEEE-57 bus system. The proposed models are implemented in GAMS environment. The numerical results substantiate the capability of the proposed SMO-ORPD problem to deal with uncertainties and to determine the best settings of control variables.     

Optimal economic operation of microgrids considering combined heat and power unit,reserve unit,and demand-side management using developed adolescent identity search algorithm

In the present study, a method is proposed to solve the problem of economic load distribution in MGs, meet the challenges arising from the use of renewable sources periodically, ensure the stable performance of MGs, and minimize the operating cost of MGs considering combined heat and power (CHP) units and reserve system. Moreover, demand-side management (DSM) as a tool is employed to reduce the operating cost of the power system. Therefore, the proposed model for optimal operation of MGs using DSM is formulated as an optimization problem. Load shifting is considered as an effective solution in DSM. Minimizing the total operating cost of the system is considered as the objective function of this problem. Problem constraints include operating and executive constraints for load shifting. Finally, the model is solved using the developed adolescent identity search algorithm (AISA). In the developed model, Powell's local search operator is employed to improve the efficiency of searching for the optimal solution. Due to the existing uncertainties in load consumption and day-ahead market price, the method is presented as a scenario-based stochastic energy management problem. The results reveal the proposed method is highly efficient in solving the problem, and load management can improve economic indicators.     

Hybrid optimization algorithm for modeling and management of micro grid connected system

In this paper, a hybrid optimization algorithm is proposed for modeling and managing the micro grid (MG) system. The management of distributed energy sources with MG is a multi-objective problem which consists of wind turbine (WT), photovoltaic (PV) array, fuel cell (FC), micro turbine (MT) and diesel generator (DG). Because, perfect economic model of energy source of the MG units are needed to describe the operating cost of the output power generated, the objective of the hybrid model is to minimize the fuel cost of the MG sources such as FC, MT and DG. The problem formulation takes into consideration the optimal configuration of the MG at a minimum fuel cost, operation and maintenance costs as well as emissions reduction. Here, the hybrid algorithm is obtained as artificial bee colony (ABC) algorithm, which is used in two stages. The first stage of the ABC gets the optimal MG configuration at a minimum fuel cost for the required load demand. From the minimized fuel cost functions, the operation and maintenance cost as well as the emission is reduced using the second stage of the ABC. The proposed method is implemented in the Matlab/Simulink platform and its effectiveness is analyzed by comparing with existing techniques. The comparison demonstrates the superiority of the proposed approach and confirms its potential to solve the problem.     

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).     

Active-reactive power scheduling of integrated electricity-gas network with multi-microgrids

Advances in natural gas-fired technologies have deepened the coupling between electricity and gas networks, promoting the development of the integrated electricity-gas network (IEGN) and strengthening the interaction between the active-reactive power flow in the power distribution network (PDN) and the natural gas flow in the gas distribution network (GDN). This paper proposes a day-ahead active-reactive power scheduling model for the IEGN with multi-microgrids (MMGs) to minimize the total operating cost. Through the tight coupling relationship between the subsystems of the IEGN, the potentialities of the IEGN with MMGs toward multi-energy cooperative interaction is optimized. Important component models are elaborated in the PDN, GDN, and coupled MMGs. Besides, motivated by the non-negligible impact of the reactive power, optimal inverter dispatch (OID) is considered to optimize the active and reactive power capabilities of the inverters of distributed generators. Further, a second-order cone (SOC) relaxation technology is utilized to transform the proposed active-reactive power scheduling model into a convex optimization problem that the commercial solver can directly solve. A test system consisting of an IEEE-33 test system and a 7-node natural gas network is adopted to verify the effectiveness of the proposed scheduling method. The results show that the proposed scheduling method can effectively reduce the power losses of the PDN in the IEGN by 9.86%, increase the flexibility of the joint operation of the subsystems of the IEGN, reduce the total operation costs by $32.20, and effectively enhance the operation economy of the IEGN.     

An improved particle swarm optimisation for unit commitment in microgrids with battery energy storage systems considering battery degradation and uncertainties

Using electric storage systems (ESSs) is known as a viable strategy to mitigate the volatility and intermittency of renewable distributed generators (DGs) in microgrids (MGs). Among different electric storage technologies, battery energy storage (BES) is considered as the best option. In unit commitment (UC) module, the set of committed dispatchable DGs along with their power, power exported to/imported from macrogrid and status and power of ESS units are determined. In this paper, BES degradation is considered in UC formulation and an efficient particle swarm optimisation with quadratic transfer function is proposed for solving UC in BES‐integrated MGs, while the uncertainties of demand, renewable generation and market price are considered and dealt with robust optimisation. UC is formulated as a multi‐objective optimisation problem whose objectives are MG operation cost and BES degradation. The resultant multi‐objective optimisation problem is converted into a single‐objective optimisation problem and the effect of weight factors on MG operation cost and BES lifecycle are investigated. The results show that by consideration of BES degradation in objective function, BES lifecycle increases from 350 to 500 and the minimum depth of charge increases from 5.5% to 34%; however, MG operation cost increases from $8717 to $8910.2. The results also show that by consideration of uncertainties, MG's operation cost increases by 8.22%.     

含DG随机出力的配电网多目标无功优化

针对分布式电源(DG)出力具有间歇性和不确定性的问题,建立了基于两点估计法(2PEM)含DG随机出力的配电网概率潮流计算模型,并将基于Pareto最优前沿解集的多目标进化算法(MOEA)与两点估计概率计算模型相结合,建立了以网损、节点电压偏移量及优化成本为目标函数的多目标无功优化模型。将该优化模型应用于IEEE33标准节点测试系统中的仿真结果表明,该方法具有较好的适应性,能为决策者提供多样性选择,增加了决策的灵活性。     

A novel maximum power point tracking technique based on fuzzy logic for photovoltaic systems

Maximum power point tracking (MPPT) techniques are considered a crucial part in photovoltaic system design to maximise the output power of a photovoltaic array. Whilst several techniques have been designed, Perturb and Observe (P&O) is widely used for MPPT due to its low cost and simple implementation. Fuzzy logic (FL) is another common technique that achieves vastly improved performance for MPPT technique in terms of response speed and low fluctuation about the maximum power point. However, major issues of the conventional FL-MPPT are a drift problem associated with changing irradiance and complex implementation when compared with the P&O-MPPT. In this paper, a novel MPPT technique based on FL control and P&O algorithm is presented. The proposed method incorporates the advantages of the P&O-MPPT to account for slow and fast changes in solar irradiance and the reduced processing time for the FL-MPPT to address complex engineering problems when the membership functions are few. To evaluate the performance, the P&O-MPPT, FL-MPPT and the proposed method are simulated by a MATLAB-SIMULINK model for a grid-connected PV system. The EN 50530 standard test is used to calculate the efficiency of the proposed method under varying weather conditions. The simulation results demonstrate that the proposed technique accurately tracks the maximum power point and avoids the drift problem, whilst achieving efficiencies of greater than 99.6%.     

Promotion strategy of clean technologies in distributed generation expansion planning

Distributed generation expansion planning (DGEP) has been frequently reported in the literature around the world. In this scope, renewable technologies which are considered as a kind of distributed generations are developing due to their environmental benefits. However, only a few renewable energies have proven to be competitive so far, while their economic viability is also limited to certain regions of the world. In this paper, an encouraging mechanism is proposed in favor of clean technologies in the planning process. This mechanism is defined based on a grant function of emission not polluted which is paid to DG owners to promote renewable and clean technologies. In the planning process, a multi-objective optimization algorithm is applied to produce a Pareto set of optimal planning schemes by taking into account different objective functions (cost and grant functions). The best planning scheme among the Pareto set is chosen based on a composite utility which are obtained through a Monte Carlo simulation of uncertain situations. Distributed generation technologies which are considered in this paper are conventional and renewable technologies, namely photovoltaic (PV), wind turbine (WT), fuel cell (FC), micro turbine (MT), gas turbine (GT), and reciprocal engine (RE). To assess the ability of the proposed method, a typical distribution system is used for expansion planning under two environmental scenarios.     

Stochastic scheduling of power system in the presence of electric vehicle and renewable sources considering security and economic indexes using an improved mutation particle swarm optimization (M-PSO) algorithm

Increased air pollution and global temperature as well as motor vehicle fuel consumption have depleted fossil fuel resources and increased environmental problems caused by the consumption of such fuels. In addition to methods such as combined heat and power (CHP) technology and distributed generation (DG) of energy at the consumption site, renewable energy sources and EVs are considered suitable methods for achieving this goal, which is prepared by the grid or battery electric energy. Generation uncertainty due to the lack of solar radiation and constant wind blow at different hours of the day is the only challenge for using renewable energies. Moreover, system reliability is a concept that refers to the safe and reliable operation of the system. In general, the wider and more important the system, the more attention that is paid to calculating its reliability in planning and decision making. This study aims to examine the problem of probabilistic power system planning by calculating the power system reliability, evaluating the effect of the presence of these vehicles on security and economic indicators and renewable energy sources, and modeling uncertainties using a Least Squares Generative Adversarial Network (LSGANs) method with generating various scenarios for solar irradiance and wind speed. Furthermore, the Kantorovich distance matrix (KDM) is used to reduce the number of generated scenarios. In the proposed model, the conditional value-at-risk (CVaR) method is implemented to assess and control the risk caused by uncertainties of the proposed problem. Using the power stored in the EV battery is evaluated to cover wind and solar energy source uncertainties.     

A fast and low cost analog maximum power point tracking method for low power photovoltaic systems

Low power photovoltaic (PV) systems are commonly used in stand-alone applications. For these systems, a simple and cost-effective maximum power point tracking (MPPT) solution is essential. In this paper, a fast and low cost analog MPPT method for low power PV systems is proposed. By using two voltage approximation lines (VALs) to approximate the maximum power point (MPP) locus, a low-complexity analog MPPT circuit can be developed. Theoretical derivation and detailed design procedure will be provided in this paper. The proposed method boasts the advantages such as simple structure, low cost, fast tracking speed and high tracking efficiency. To validate the correctness of the proposed method, simulation and experimental results of an 87 W PV system will also be provided to demonstrate the effectiveness of the proposed technique.     

A novel decentralized method based on the system engineering concept for reliability‐security constraint unit commitment in restructured power environment

In the deregulated power environment, including Central operator (CO) and Micro Grids (MGs), different parts of the network are dedicated to the private sector, and each of them seeks to increase their profits independently. The CO and MGs should cooperate and collaborate in terms of operating, security and reliability in the whole power system. This article proposes a new method based on a System of System (SoS) concept for the secure and economic hourly generation scheduling to find optimal operational point. The main methodology includes three steps. In the first step, the power system is divided into several subsystems by using a spectral clustering partitioning technique to reduce converge time by decentralizes methods. And also load forecasting based on a Gaussian probability distribution function to avoid conventional calculation and considering uncertainty of the loads has been presented. To find a similar scenario, and reduction scenario with low probability, the probabilistic method has been addressed. The main contribution of this method is removing scenarios with low value of probabilities and scenarios which are similar to each other. In fact, the reduced set must include scenarios which are scattered appropriately in the uncertain space while holding high probabilities. In order to estimate the similarity (distance) between two scenarios the Kantorovich distance is implemented. In the second step, the hierarchical Bi‐level optimization approach is used to execute the decentralized decision making to solve the Security Constraints Unit Commitment (SCUC) problem between CO and MGs. Regarding all physical relations and shared data among CO and MGs, the SoS concept and Bi‐level optimization are presented to find the optimal operating point of autonomous systems. In the third step, a random number of generators will be select. Hence, the initial iteration number is set. In this step, sampling from state space to classifying reliability object achieved (The expected energy not supplied and loss of load probability are the reliability criterion). The presented method is evaluated using a 6‐bus, the RTS 24‐bus, 118‐bus, and 4672‐bus as an IEEE test systems. The suggested structure has been implemented by GAMS, and the results illustrate the usefulness of the presented methodology. To comparing proposed approach with the centralized method, the results illustrate improving total operational costs and security (in RTS‐24($603,209), 118 bus ($2 562 154) and 4672‐bus ($9 185 168)) in scenario 3 near to 9%, 10% and 8% respectively. Similarly, by comparison (in three test systems) with genetic algorithm these improvements are near to 23%, 22% and 13% respectively.     

Optimal distributed energy resources planning in a competitive electricity market: Multiobjective optimization and probabilistic design

This paper presents a probabilistic multiobjective framework for optimal distributed energy resources (DERs) planning in the distribution electricity networks. The proposed model is from the distribution company (DISCO) viewpoint. The projected formulation is based on nonlinear programming (NLP) computation. The proposed design attempts to achieve a trade-off between minimizing the monetary cost and minimizing the emission of pollutants in presence of the electrical load as well as electricity market prices uncertainties. The monetary cost objective function consists of distributed generation (DG) investment and operation cost, payment toward loss compensation as well as payment for purchased power from the network. A hybrid fuzzy C-mean/Monte-Carlo simulation (FCM/MCS) model is used for scenario based modeling of the electricity prices and a combined roulette-wheel/Monte-Carlo simulation (RW/MCS) model is used for generation of the load scenarios. The proposed planning model considers six different types of DERs including wind turbine, photovoltaic, fuel cell, micro turbine, gas turbine and diesel engine. In order to demonstrate the performance of the proposed methodology, it is applied to a primary distribution network and using a fuzzified decision making approach, the best compromised solution among the Pareto optimal solutions is found.     

Statistical metrics for assessing the quality of wind power scenarios for stochastic unit commitment

In power systems with high penetration of wind generation, probabilistic scenarios are generated for use in stochastic formulations of day‐ahead unit commitment problems. To minimize the expected cost, the wind power scenarios should accurately represent the stochastic process for available wind power. We employ some statistical evaluation metrics to assess whether the scenario set possesses desirable properties that are expected to lead to a lower cost in stochastic unit commitment. A new mass transportation distance rank histogram is developed for assessing the reliability of unequally likely scenarios. Energy scores, rank histograms and Brier scores are applied to alternative sets of scenarios that are generated by two very different methods. The mass transportation distance rank histogram is best able to distinguish between sets of scenarios that are more or less calibrated according to their bias, variability and autocorrelation. Copyright © 2015 John Wiley & Sons, Ltd.     

A multiple objective linear programming model for power generation expansion planning

The planning of new units for electrical power generation is a problem which involves different and conflicting aspects. Besides cost, security issues and environmental concerns must be explicitly incorporated into the models. In this way mathematical models become more realistic, and they enhance the decision maker's comprehension of the complex and conflicting nature of the distinct aspects of the problem. A multiple objective linear programming model for power generation expansion planning is presented. The model considers three objective functions (net present cost of the expansion plans, reliability of the supply system, and environmental impacts) and three categories of constraints (load requirements, operational restrictions and budget). Three generating technologies are considered for power system expansion: oil, nuclear and coal.     

Generating short‐term probabilistic wind power scenarios via nonparametric forecast error density estimators

Forecasts of available wind power are critical in key electric power systems operations planning problems, including economic dispatch and unit commitment. Such forecasts are necessarily uncertain, limiting the reliability and cost‐effectiveness of operations planning models based on a single deterministic or “point” forecast. A common approach to address this limitation involves the use of a number of probabilistic scenarios, each specifying a possible trajectory of wind power production, with associated probability. We present and analyze a novel method for generating probabilistic wind power scenarios, leveraging available historical information in the form of forecasted and corresponding observed wind power time series. We estimate nonparametric forecast error densities, specifically using epi‐spline basis functions, allowing us to capture the skewed and nonparametric nature of error densities observed in real‐world data. We then describe a method to generate probabilistic scenarios from these basis functions that allows users to control for the degree to which extreme errors are captured. We compare the performance of our approach to the current state‐of‐the‐art considering publicly available data associated with the Bonneville Power Administration, analyzing aggregate production of a number of wind farms over a large geographic region. Finally, we discuss the advantages of our approach in the context of specific power systems operations planning problems: stochastic unit commitment and economic dispatch. Our methodology is embodied in the joint Sandia–University of California Davis Prescient software package for assessing and analyzing stochastic operations strategies.