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Home Energy Management System Using Ant Colony Optimization Technique in Microgrid
Abstract
From previous years, the research on usage of renewable energy sources (RES), specially photo voltaic (PV) arrays. This paper is based on home energy management system (HEMS). We propose a grid connected microgrid to fulfill the load demand of residential area. We have consider fifteen homes with six appliance for each home, the appliances are taken as the base load. For bill calculation, real time pricing (RTP) tariff is used. Ant colony optimization (ACO) is used for the scheduling of appliances. To fulfill the load demand; Wind turbine (WT), PV, micro turbine (MT), fuel cell (FC) and diesel generator (DG) are used. Energy storage devices are used with generators to store excessive energy. Also, we propose penalty and incentive (PI) mechanism to reduce the overall cost. Objectives of the paper are cost and peak to average ratio (PAR). The simulation results show better performance with our optimization technique rather than without any technique.
... Some papers (e.g., [6,22]) neglect the term (((NOCT − 20)/800)G) to give a simpler equation. Other simplified expressions for the output can be found in [6,7,32]. A detailed expression is presented in [14]. ...
... is equation is used in [1,9,22,42]. Other expressions, namely, equation (3), integrate power coefficients and air density and are given in [11,32,36,43]. A more detailed expression is presented in [36,43]: Title, abstract, and keywords: (microgrid OR micro-grid OR "smart building" OR "smart grid") AND ("energy management" OR "energy balance" OR "load balance") AND (optimal OR optimization) Find articles with these terms: (PV OR wind OR solar OR "renewable energy") AND storage Years: 2015-2018 ...
... A variety of heuristics were used in the μ grid field. We cite multiobjective self-adaptive differential evolution algorithm (MOSaDE) [1], genetic algorithms (GA) [2], ant colony optimization (ACO) [32], backtracking search optimization algorithm (BSOA) [14], and particle swarm optimization (PSO) [18,24] and [21,31,41]. e authors in the latter references used different versions of the same algorithm which are, respectively, modified PSO, regrouping PSO, and multiobjective PSO. ...
Having neither precise definition nor a commonly accepted scope, the term “MicroGrid” tends to be used differently across researchers and practitioners alike. The management of energy usage within a microgrid is one of the topics that was handled from numerous perspectives. This study presents systematic literature review (SLR) of research on architectures and energy management techniques for microgrids, providing an aggregated up-to-date catalogue of solutions suggested by the scientific community. The SLR incorporated 45 papers selected according to inclusion/exclusion criteria and defined a priori. The selection process was based on an automated search and covered three known digital libraries. The extraction process covers three main questions. (i) The architectures of microgrids including their components, their bus configuration, and the adopted utility grid policy. (ii) The employed methods to ensure an optimal usage of energy under uncertainty. (iii) The confronted challenges and constraints of the suggested strategies. The findings of this SLR indicate a great diversity of methods and a rich background. Finally, the SLR suggests that future research should take into account the uncertainty aspect relating to energy management rather than the direct use of historical data as it is commonly done in most research papers. A sensitivity analysis should be provided in the latter case.
... In [10], an improved version of the particle swarm optimization (PSO) algorithm is used to find optimal decision variables to minimize the operational cost of DGs, cost of responsive loads, and exchange power between the MG and the upstream grid. The operation of a residential MG consisting of microturbine, wind turbine (WT), and photovoltaic (PV) is optimized based on the MG's cost and peak reduction in [11], by using the ant colony optimization algorithm (ACO) to solve the MG energy management problem. In [12], artificial bee colony optimization (ABC) algorithm has been developed for solving deterministic MG energy management problems with the aim of operational cots and power loss minimization considering renewable and dispatchable generations. ...
... Furthermore, the constraints (10), (11), (12) can be converted to (31), (32), and (33) ...
In modern power systems, microgrids play a pivotal role with several economical, technical, and environmental benefits. However, there are still challenges that need to be properly addressed, including: i) accurate modeling of the uncertain parameters behavior, ii) considering the correlation between the random variables, and iii) find the optimal solutions with low computational burden. To address these issues, this paper proposes a nonparametric-correlative stochastic framework for microgrids (MGs) energy management. The proposed method imposes no assumption on the probability density function of renewable generations and electrical load consumption. To this end, an improved kernel density estimator (IKDE) is presented to estimate the probability density function (PDF) of uncertain parameters, e.g., renewable generations and load. To account for the correlation between the uncertain parameters, Cholesky decomposition is utilized. Furthermore, a multi-objective MG energy management problem considering reliability has been reformulated, and to solve the problem, a sine-cosine optimization algorithm (SCOA) is developed. Numerical results demonstrate the effectiveness and superiority of the proposed stochastic framework through comparison with several optimization algorithms by reducing the total cost of MG more than 11% in comparison with several metaheuristic algorithms and stochastic frameworks with less than 1% error.
... Still, in Rahim et al. (2016a,b) the security issues between the utility and user should be improved. This (Fatima et al., 2017) is based on a microgrid connected to a grid using a point of standard coupling (PCC). The proposed ACO technique has a low PAR by scheduling (with incentive mechanism and penalty) and high PAR without scheduling appliances. ...
Present advancements in the power systems paved way for introducing the smart grid (SG). A smart grid is beneficial to consumers which enables the bi-directional flow of information between the utility and customer. Demand-side management (DSM) techniques are crucial as load-side management techniques to attain the better stability of the grid. Home energy management systems (HEMS) play a indispensable part in the DSM. Countless traditional optimization techniques are utilized to implement HEMS, but the limitations of traditional Math heuristic methods gave rise to a concept-based optimization techniques called the Meta heuristic methods. Recent advancements introduced smart optimization techniques powered by Artificial Intelligence (AI). This article elucidates the applications of AI-based optimization techniques and their advantages over other methods. Various Machine learning (ML) and Deep Learning (DL) algorithms and their utilization for HEMS are discussed in brief.
... Simulations proved the efficacy of the suggested algorithm. A new hybrid technique is proposed in [42] to predict the power output of a wind farm in Binaloud. The hybridization of ACO and PSO is done to optimize the proposed model. ...
Smart Gird is a technology that has brought many advantages with its evolution. Smart Grid is indispensable as it will lead us towards environmentally sustainable economic growth. Home energy management in Smart Grid is a hot research topic now a days. It aims at reducing the energy cost of users, gaining energy self-reliance and decreasing Greenhouse gas emissions. Renewable energy technologies nowadays are best suitable for off grid services without having to build extensive and complicated infrastructure. With the advent of Smart Grid (SG), the occupants have the opportunity to integrate with renewable energy sources (RESs) and to actively take part in demand side Management (DSM). This review paper is comprehensive study of various optimization techniques and their implementation with respect to electricity cost diminution, load balancing, power consumption and user's comfort maximization etc. for Home Energy Management in Smart Grid. This paper summarizes recent trends of energy usage from hybrid renewable energy integrated sources. It discusses several methodologies and techniques for hybrid renewable energy system optimization.
... The paper [1], [2] made a significant contribution by the collation of cloud fog based infrastructure with SG. They have used Cloud Analyst simulator for testing of their proposed scenario. ...
Energy is among the most valuable resource in the world that need to be consumed in an optimized manner. For making intelligent decisions in energy consumption Smart Grid (SG) is introduced. One of the key components of SG is communication. Cloud-Fog based environment is the most popular communication architecture nowadays. Keeping the focus on this point this article proposed an integration of Cloud-Fog based environment with Micro Grid (MG) for effective resource management. For experimentation, the word is divided into 6 regions based on the division of continents. Each region contains 6 clusters and 3 fogs connected to each of them with MG and centralized cloud. Cloud Analyst simulator is used for testing of our proposed scenario. To cater the huge load on fogs a new load balancing technique Shortest Load First(SLF) is introduced in the simulator. The load balancer technique is used to manage the requests on fogs whereas the dynamic service proximity policy is used for connection of clusters with fogs.
The integration of storage technologies into the hybrid energy system (HES) offers significant stability in delivering electricity to a remote community. In addition, the benefits of using storage devices for achieving high renewable energy (RE) contribution to the total energy supply are also paramount. The present study provides a detailed review on the utilization of pump hydro storage (PHS) related to the RE‐based stand‐alone and grid‐connected HESs. The PHS‐based HESs have been analyzed considering the technical details, including reliability, cost‐effectiveness, and environmental indicators. The optimization techniques are critically employed to the PHS‐based systems, including metaheuristics intelligent techniques, commercially available optimization and simulation tools, and recent advanced optimization tools. A comparative analysis of PHS integration to the stand‐alone and grid‐connected systems based on techno‐economic and environmental indicators over other storage technologies is also presented. The key technical and environmental challenges related to the PHS‐based HES are identified. Results from the recent research studies indicate that the PHS‐based HESs offer significant cost and environmental benefits over battery storage technologies. The study identifies that the particle swarm optimization (PSO) is the mostly appreciated optimizing technique for cost effective energy supply and environmental aspects followed by HOMER and GA.
Energy is among the most valuable resource in the world that need to be consumed in an optimized manner. For making intelligent decisions in energy consumption Smart Grid (SG) is introduced. One of the key components of SG is communication. Cloud-Fog based environment is the most popular communication architecture nowadays. Keeping the focus on this point this article proposed an integration of Cloud-Fog based environment with Micro Grid (MG) for effective resource management. For experimentation, the word is divided into 6 regions based on the division of continents. Each region contains 6 clusters and 3 fogs connected to each of them with MG and centralized cloud. Cloud Analyst simulator is used for testing of our proposed scenario. To cater the huge load on fogs a new load balancing technique Shortest Load First (SLF) is introduced in the simulator. The load balancer technique is used to manage the requests on fogs whereas the dynamic service proximity policy is used for connection of clusters with fogs.
The use of a new control method for grid-connected inverters for reducing the output current harmonic distortion in a wide range of grid-connected distributed generation (DG) applications, including wind turbine (WT) and fuel cell (FC) inverters is proposed in this paper. The control method designed to eliminate main harmonics in a microgrid (MG) and between MG and point of common coupling (PCC) and responsible for the correction of the system unbalance. Another advantage of the proposed control method is that it can be easily adopted into the DG control system without the installation of extra hardware. The proposed control method is comprised of the synchronous reference frame method (SRF). Results from the proposed control method are provided to show the feasibility of the proposed approach.
We present a geographic information system (GIS)-based tool for estimating the
performance of photovoltaic (PV) mini-grid system over large geographical areas. The methodology
consists of geospatial analysis and mapping of the energy output and reliability of PV mini-grid
system. The algorithm uses a combination of hourly solar radiation data from satellites combined
with measured data on PV module and battery performance and estimated electricity consumption
data. The methods also make it possible to optimize the PV array and battery storage size for a given
location. Results are presented for an area covering Africa and most of Southern and Central Asia.
We also investigate the effects of using Li-ion batteries instead of the traditional lead-acid batteries.
The use of our spatial analysis as decision support tool could help governments, local authorities
and non-governmental organizations to investigate the suitability of PV mini-grids for electrification
of regions where access to electricity is lacking. In this way it is possible to identify areas where PV
mini-grids are most suitable.
In this work, we evaluate performance of home energy management system(HEM) by using three meta-heuristic optimization techniques: Harmony search algorithm (HSA), Bacterial foraging optimization (BFO) and Enhanced deferential evolution(EDE). We categorize appliances into three groups on the basis of their energy consumption pattern. Real time pricing (RTP) scheme is used for electricity bill calculation. Our objectives are to minimize electricity cost, energy consumption, reduction in peak to average ratio while maximizing user comfort. However, there exists a trade-off between different objectives. Our simulation results show that there exist a trade-off between user comfort and cost. Results also show that in terms of cost HSA perform better among other techniques.
A distributed power system with renewable energy sources is very popular in recent years due to the rapid depletion of conventional sources of energy. Reasonable sizing for such power systems could improve the power supply reliability and reduce the annual system cost. The goal of this work is to optimize the size of a stand-alone hybrid photovoltaic (PV)/wind turbine (WT)/battery (B)/hydrogen system (a hybrid system based on battery and hydrogen (HS-BH)) for reliable and economic supply. Two objectives that take the minimum annual system cost and maximum system reliability described as the loss of power supply probability (LPSP) have been addressed for sizing HS-BH from a more comprehensive perspective, considering the basic demand of load, the profit from hydrogen, which is produced by HS-BH, and an effective energy storage strategy. An improved ant colony optimization (ACO) algorithm has been presented to solve the sizing problem of HS-BH. Finally, a simulation experiment has been done to demonstrate the developed results, in which some comparisons have been done to emphasize the advantage of HS-BH with the aid of data from an island of Zhejiang, China.
This paper presents real time information based energy management algorithms to reduce electricity cost and peak to average ratio (PAR) while preserving user comfort in a smart home. We categorize household appliances into thermostatically controlled (tc), user aware (ua), elastic (el), inelastic (iel) and regular (r) appliances/loads. An optimization problem is formulated to reduce electricity cost by determining the optimal use of household appliances. The operational schedules of these appliances are optimized in response to the electricity price signals and customer preferences to maximize electricity cost saving and user comfort while minimizing curtailed energy. Mathematical optimization models of tc appliances, i.e., air-conditioner and refrigerator, are proposed which are solved by using intelligent programmable communication thermostat ( iPCT). We add extra intelligence to conventional programmable communication thermostat (CPCT) by using genetic algorithm (GA) to control tc appliances under comfort constraints. The optimization models for ua, el, and iel appliances are solved subject to electricity cost minimization and PAR reduction. Considering user comfort, el appliances are considered where users can adjust appliance waiting time to increase or decrease their comfort level. Furthermore, energy demand of r appliances is fulfilled via local supply where the major objective is to reduce the fuel cost of various generators by proper scheduling. Simulation results show that the proposed algorithms efficiently schedule the energy demand of all types of appliances by considering identified constraints (i.e., PAR, variable prices, temperature, capacity limit and waiting time).
Microgrid is one practical infrastructure to integrate Distributed Generations (DGs) and local loads. Its optimal operating strategy has aroused great attention in recent years. This paper mainly focuses on the multi-objective optimization of DGs in microgrid by using self-adaptive genetic algorithm (GA) and fuzzy decision. Five objective functions are taken into account comprising voltage offset, transmission loss, construction cost, purchase cost and the environmental cost. In the algorithm, selfadaptation in population size, mutation probability, selection and standardization of objective functions is developed to enhance the speed and efficiency of the algorithm. Moreover, fuzzy decision is applied to determine the final solution. Simulation results show this algorithm can effectively find the optimal solution and improve the real-time control of microgrid, which implies the possibility of potential applications in microgrid energy management system.
The demand rise, climate change, growing fuel costs, outdated power system infrastructures, and new power generation technologies have made renewable distribution generators very attractive in recent years. Because of increasing penetration level of renewable energy sources in addition to growing amount of new electrical demand, such as electrical vehicles, the power system may face serious problems and challenges in the near future. A revolutionized new power grid, called smart grid, has been formed as a solution to these problems. The smart grid, equipped with modern communication and computation infrastructures, can coordinate different parts of the power system to enhance energy efficiency, reliability, and quality, while decreasing the energy cost. Since the conventional distribution networks had been deprived of smart infrastructures, much research has been recently done in the distribution part of the smart grid, called smart distribution grid (SDG). This paper surveys contemporary literature in SDG from the perspective of electricity market in addition to power system considerations. For this purpose, this paper reviews current demand side management methods, supply side management methods, and electrical vehicle charging and discharging techniques in SDG and also discusses their drawbacks. We also present future research directions to tackle new and existing challenges in the SDG.
DGs are placed for the purpose of real power loss minimization and voltage improvement in distribution network system. This paper presents a recent optimization technique i.e. teaching learning based optimization (TLBO) technique for finding the optimal size and location of Distributed Generation (DG) in radial distribution system (RDS). The optimal location and size of DG is analyzed considering voltage stability index as an objective function. The superiority of the proposed approach has been shown by comparing the results with GA and PSO methods in RDS. The comparison is done using system performances such as the real power loss and voltage profile of RDS. In this paper, performance analysis is carried out considering IEEE 33 bus and 69 buses as the test system.
Micro-grids have spread in many distribution systems worldwide. They offer safe and reliable operation for their consumers by managing the micro-grids’ power generation and trading with the main grid. Furthermore, micro-grids can help in integrating and promoting for Renewable Energy Sources (RES) and reducing the environmental impacts of traditional centralized generation. This paper proposes multiple environment dispatch problem solution in micro-grids using Ant Colony Optimization (ACO) technique to solve the generation dispatch problem. A combined cost optimization scheme is investigated to minimize both operational cost and emission levels while satisfying the micro-grid’s load demand. Furthermore, the proposed multiple environment dispatch problem solution is used to evaluate promoting RES implementation in micro-grids despite of their high capital cost using the combined economic emission dispatch problem. The proposed multiple environment dispatch solution was implemented using MatLab and tested on two case studies with and without RES-Wind Turbine-Solar (WT-PV). The obtained results from the proposed technique are compared with those calculated using other Techniques; Gradient to evaluate the proposed method. The outcomes are evaluated and discussed. Finally, conclusions are reported.
This paper presents a microgrid (MG) energy management strategy by considering renewable energy and battery storage systems. Renewable energy, including wind power generation and solar power generation, is integrated into the distribution network, for which is formulated the optimal dispatch model of mixed-power generation by considering the charging/discharging scheduling of battery storage systems. The MG system has an electrical link for power exchange between the MG and the utility during different hours of the day. Based on the time-of-use (TOU) and all technical constraints, an enhanced bee colony optimization (EBCO) is proposed to solve the daily economic dispatch of MG systems. In the EBCO procedure, the self-adaption repulsion factor is embedded in the bee swarm of the BCO in order to improve the behavior patterns of each bee swarm and increase its search efficiency and accuracy in high dimensions. Different modifications in moving patterns of EBCO are proposed to search the feasible space more effectively. EBCO is used for economic energy management of grid-connected and stand-alone scenarios, and the results are compared to those in previous algorithms. In either grid-connected or stand-alone scenarios, an optimal MG scheduling dispatch is achieved using micro-turbines, renewable energy and battery storage systems. Results show that the proposed method is feasible, robust and more effective than many previously-developed algorithms.
This paper is an extensive survey of the Bee Colony Optimization (BCO)
algorithm, proposed for the first time in 2001. BCO and its numerous variants
belong to a class of nature-inspired meta-heuristic methods, based on the
foraging habits of honeybees. Our main goal is to promote it among the wide
operations research community. BCO is a simple, but efficient meta-heuristic
technique that has been successfully applied to many optimization problems,
mostly in transport, location and scheduling fields. Firstly, we shall give a
brief overview of the other meta-heuristics inspired by bees’ foraging
principles pointing out the differences between them. Then, we shall provide
the detailed description of the BCO algorithm and its modifications,
including the strategies for BCO parallelization, and giving the preliminary
results regarding its convergence. The application survey is elaborated in
Part II of our paper. [Projekat Ministarstva nauke Republike Srbije, br.
OI174010, br. OI174033 i br. TR36002]
Smart grid is advancing power grids significantly, with higher power generation efficiency, lower energy consumption cost, and better user experience. Microgrid utilizes distributed renewable energy generation to reduce the burden on utility grids. This paper proposes an energy ecosystem; a cost-effective smart microgrid based on intelligent hierarchical agents with dynamic demand response (DR) and distributed energy resource (DER) management. With a dynamic update mechanism, DR automatically adapts to users' preference and varying external information. The DER management coordinates operations of micro combined heat and power systems ($boldsymbol {mu }$ CHPs), and vanadium redox battery (VRB) according to DR decisions. A two-level shared cost-led $boldsymbol {mu }$ CHPs management strategy is proposed to reduce energy consumption cost further. VRB discharging is managed to be environment-adaptive. Simulations and numerical results show the proposed system is very effective in reducing the energy consumption cost while satisfying user's preference.
Energy management means to optimize one of the most complex and important technical creations that we know: the energy system. While there is plenty of experience in optimizing energy generation and distribution, it is the demand side that receives increasing attention by research and industry. Demand Side Management (DSM) is a portfolio of measures to improve the energy system at the side of consumption. It ranges from improving energy efficiency by using better materials, over smart energy tariffs with incentives for certain consumption patterns, up to sophisticated real-time control of distributed energy resources. This paper gives an overview and a taxonomy for DSM, analyzes the various types of DSM, and gives an outlook on the latest demonstration projects in this domain.
The use of a new control method for grid-connected inverters for reducing the output current harmonic distortion in a wide range of grid-connected distributed generation (DG) applications, including wind turbine (WT) and fuel cell (FC) inverters is proposed in this paper. The control method designed to eliminate main harmonics in a microgrid (MG) and between MG and point of common coupling (PCC) and responsible for the correction of the system unbalance. Another advantage of the proposed control method is that it can be easily adopted into the DG control system without the installation of extra hardware. The proposed control method is comprised of the synchronous reference frame method (SRF). Results from the proposed control method are provided to show the feasibility of the proposed approach. © 2017 Institute of Advanced Engineering and Science. All rights reserved.
A dynamic parameter adaptation methodology for Ant Colony Optimization (ACO) based on interval type-2 fuzzy systems is presented in this paper. The idea is to be able to apply this new ACO method with parameter adaptation to a wide variety of problems without the need of finding the best parameters for each particular problem. We developed several fuzzy systems for parameter adaptation and a comparison was made among them to decide on the best design. The use of fuzzy logic is to control the diversity of the solutions, and in this way controlling the exploration and exploitation abilities of ACO. The travelling salesman problem (TSP) and the design of a fuzzy controller for an autonomous mobile robot are the benchmark problems used to test the proposed methodology.
In this paper an energy management technique for Hybrid Renewable Energy System (HRES) connected with AC load using Adaptive Neuro Fuzzy Interference System (ANFIS) is proposed. This algorithm is developed with an aim of increasing the power transfer capability between the source side and load side and it offers several benefits like the enhanced predicting capability, degradation in complexity as well as the randomization and so forth. In this work, photovoltaic (PV) system, Wind Generating System (WGS), Fuel Cell (FC), Ultra Capacitor (UC) and the battery are considered as the energy sources. The ANFIS technique is trained with the inputs such as the previous instant energy of the available sources and the required load demand of the current time and the corresponding target reference power of the sources and storage devices. According to the load variation, the proposed method makes the appropriate control signals at the testing time to manage the energy of the HRES. A STATCOM based voltage regulation and harmonic mitigation is introduced. The implementation of the system elements and control method has been done in MATLAB/Simulink and the performance of the proposed method is analysed by using different environmental and load test conditions. The results of the test cases confirms that the proposed control technique is effective in prediction of energy required for the next instant and manages the energy flow among HRES power sources and energy storage devices.
In this paper, we comparatively evaluate the performance of home energy management controller which is designed on the basis of heuristic algorithms; genetic algorithm (GA), binary particle swarm optimization (BPSO) and ant colony optimization (ACO). In this regard, we introduce a generic architecture for demand side management (DSM) which integrates residential area domain with smart area domain via wide area network. In addition, problem formulation is carried via multiple knapsack problem. For energy pricing, combined model of time of use tariff and inclined block rates is used. Simulation results show that all designed models for energy management act significantly to achieve our objections and proven as a cost-effective solution to increase sustainability of smart grid. GA based energy management controller performs more efficiently than BPSO based energy management controller and ACO based energy management controller in terms of electricity bill reduction, peak to average ratio minimization and user comfort level maximization.
Nowadays, Distributed Generations (DGs) and microgrids play a significant role in development of power systems, but DGs produce several protection problems especially in optimal coordination of Directional Overcurrent Relays (DORs). Optimal coordination of DORs is a highly constrained Non-Linear Programming Problem (NLPP), which has attracted attention of many research studies in recent years. Cuckoo Optimization Algorithm (COA) is introduced as an optimization tool based on behavior patterns of Cuckoos. In this paper, COA and Linear Programming (LP) are combined as a new hybrid COA-LP optimization algorithm in order to optimize the coordination protection of DORs in microgrids and find the optimum value of Fault Current Limiter (FCL) at the Point of Common Coupling (PCC). The optimum settings of DORs are obtained for both modes of operation: grid-connected and island mode. The proposed algorithm is applied to Canadian distribution benchmark and a modified IEEE 14 bus networks and compared with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The obtained results show that the computational efficiency of the proposed COA-LP is better than PSO and GA in terms of speed and total operating time of DORs.
Today, energy is the most valuable resource, new methods and techniques are being discovered to fulfill the demand of energy. However, energy demand growth causes a serious energy crisis, especially when demand is comparatively high and creates the peak load. This problem can be handled by integrating Demand Side Management (DSM) with traditional Smart Grid (SG) through two way communication between utility and customers. The main objective of DSM is peak load reduction where SG targets cost minimization and user comfort maximization. In this study, our emphasis is on cost minimization and load management by shifting the load from peak hours toward the off peak hours. In this underlying study, we adapt hybridization of two optimization approaches, Bacterial Foraging (BFA) and Genetic Algorithm (GA). Simulation results verify that the adapted approach reduces the total cost and peak average ratio by shifting the load on off peak hours with very little difference between minimum and maximum 95% confidence interval.
In this paper, a hybrid algorithm consisting of particle swarm optimization and pattern search algorithm is proposed to evaluate and optimize the design and operation of microgrids (MGs) in combined gas and power networks. Key performance indicators (KPIs) are modeled and proposed to evaluate and assess MGs. The paper begins by proposing a comprehensive study to define KPIs, which are used to evaluate the following MG parameters: economical efficiency, reliability, environmental conservation, and power quality. Multi-objective evaluation functions are then developed by building a relationship matrix of MG and KPI components. The results are then displayed as optimized power generation percentages for each technology with values for four KPI categories: cost, quality, reliability and environmental friendliness. Two case studies are examined in this paper; both the province of Ontario and Toronto regional zone under all system parameters with varying percentage of generation via gas technology. Results indicated that the optimal scenario for both Ontario and Toronto was achieved at hybrid PSO–patern search percentage generation via gas technology with improved cost KPI and other KPIs remaining approximately constant. Copyright
In this paper, a particle swarm optimization (PSO)-based power dispatch algorithm is proposed to deal with the energy management problem of the hybrid generation system (HGS). For conventional PSO method, the search space is only defined by inequality constraints. However, as for power dispatch problems, it is vital to maintain power balance, which can be represented as an equality constraint. To address this issue, a roulette wheel re-distribution mechanism is proposed. With this re-distribution mechanism, unbalanced power can be reallocated to more superior element and the searching diversity can be preserved. In addition, the effect of depth of discharge on the life cycle of the battery bank is also taken into account by developing a penalty mechanism. The proposed method is then applied to a HGS consisting of photovoltaic array, wind turbine, microturbine, battery banks, utility grid and residential load. To validate the effectiveness and correctness of the proposed method, simulation results for a whole day will also be provided. Comparing with three other power dispatching methods, the proposed method can achieve the lowest accumulated cost.
Recently, the use of wind generation has rapidly increased in micro-grids. Due to the fluctuation of wind power, it is difficult to schedule wind turbines (WTs) with other distributed energy resources (DERs). In this paper, we propose an expert energy management system (EEMS) for optimal operation of WTs and other DERs in an interconnected micro-grid. The main purpose of the proposed EEMS is to find the optimal set points of DERs and storage devices, in such a way that the total operation cost and the net emission are simultaneously minimized. The EEMS consists of wind power forecasting module, smart energy storage system (ESS) module and optimization module. For optimal scheduling of WTs, the power forecasting module determines the possible available capacity of wind generation in the micro-grid. To do this, first, an artificial neural network (ANN) is used to forecast wind speed. Then, the obtaining results are used considering forecasting uncertainty by the probabilistic concept of confidence interval. To reduce the fluctuations of wind power generation and improve the micro-grid performances, a smart energy storage system (ESS) module is used. For optimal management of the ESS, the comprehensive mathematical model with practical constraints is extracted. Finally, an efficient modified Bacterial Foraging Optimization (MBFO) module is proposed to solve the multi-objective problem. An interactive fuzzy satisfying method is also used to simulate the trade-off between the conflicting objectives (cost and emission). To evaluate the proposed algorithm, the EEMS is applied to a typical micro-grid which consists of various DERs, smart ESS and electrical loads. The results show that the EEMS can effectively coordinate the power generation of DERs and ESS with respect to economic and environmental considerations.
In recent years, various heuristic optimization methods have been developed. Many of these methods are inspired by swarm behaviors in nature. In this paper, a new optimization algorithm based on the law of gravity and mass interactions is introduced. In the proposed algorithm, the searcher agents are a collection of masses which interact with each other based on the Newtonian gravity and the laws of motion. The proposed method has been compared with some well-known heuristic search methods. The obtained results confirm the high performance of the proposed method in solving various nonlinear functions.
Integrating Price Responsive Demand Into the Unit Commitment Problem
- Zhe Vamsi Krishna Tumuluru
- Danny H K Huang
- Tsang
Vamsi Krishna Tumuluru, Zhe Huang and Danny H. K. Tsang, Integrating Price Responsive
Demand Into the Unit Commitment Problem, IEEE Transactions on Smart Grid 5, November
2014.
Optimization and Energy Management of a Microgrid Based on Frequency Communications
- A Varela
- Souto
A. Varela Souto, Optimization and Energy Management of a Microgrid Based on Frequency
Communications, http://repository.tudelft.nl/, 2016.