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نتیجه جستجو - microgrid energy management

تعداد مقالات یافته شده: 9
ردیف عنوان نوع
1 Optimization strategies for Microgrid energy management systems by Genetic Algorithms
استراتژی های بهینه سازی برای سیستم های مدیریت انرژی میکرو گرید توسط الگوریتم های ژنتیک-2020
Grid-connected Microgrids (MGs) have a key role for bottom-up modernization of the electric distribution network forward next generation Smart Grids, allowing the application of Demand Response (DR) services, as well as the active participation of prosumers into the energy market. To this aim, MGs must be equipped with suitable Energy Management Systems (EMSs) in charge to efficiently manage in real time internal energy flows and the connection with the grid. Several decision making EMSs are proposed in literature mainly based on soft computing techniques and stochastic models. The adoption of Fuzzy Inference Systems (FISs) has proved to be very successful due to their ease of implementation, low computational run time cost, and the high level of interpretability with respect to more conventional models. In this work we investigate different strategies for the synthesis of a FIS (i.e. rule based) EMS by means of a hierarchical Genetic Algorithm (GA) with the aim to maximize the profit generated by the energy exchange with the grid, assuming a Time Of Use (TOU) energy price policy, and at the same time to reduce the EMS rule base system complexity. Results show that the performances are just 10% below to the ideal (optimal) reference solution, even when the rule base system is reduced to less than 30 rules.
Keywords: Microgrids | Genetic algorithms | Fuzzy systems | Energy management systems
مقاله انگلیسی
2 Energy management system for hybrid PV-wind-battery microgrid using convex programming, model predictive and rolling horizon predictive control with experimental validation
سیستم مدیریت انرژی برای ریز شبکه هیبریدی PV-باد باتری با استفاده از برنامه نویسی محدب ، مدل پیش بینی و کنترل پیش بینی افق نورد با اعتبارسنجی آزمایشی-2020
The integration of energy storage technologies with renewable energy systems can significantly reduce the operating costs for microgrids (MG) in future electricity networks. This paper presents a novel energy management system (EMS) which can minimize the daily operating cost of a MG and maximize the self-consumption of the RES by determining the best setting for a central battery energy storage system (BESS) based on a defined cost function. This EMS has a two-layer structure. In the upper layer, a Convex Optimization Technique is used to solve the optimization problem and to determine the reference values for the power that should be drawn by the MG from the main grid using a 15 min sample time. The reference values are then fed to a lower control layer, which uses a 1 min sample time, to determine the settings for the BESS which then ensures that the MG accurately follows these references. This lower control layer uses a Rolling Horizon Predictive Controller and Model Predictive Controllers to achieve its target. Experimental studies using a laboratory-based MG are implemented to demonstrate the capability of the proposed EMS.
Keywords: Microgrid Energy Management | Battery Energy Storage System | Real-Time Battery Control | Convex Optimization | Model Predictive Control | Rolling Horizon Predictive Controller | Adaptive Autoregression Algorithm
مقاله انگلیسی
3 A multi-objective voltage stability constrained energy management system for isolated microgrids
یک سیستم مدیریت انرژی با ثبات ولتاژ چند هدف محدود شده برای ریز شبکه های جدا شده-2020
Nowadays, microgrids are more likely to operate in an isolated state, and it is of outmost importance to consider various security issues in the management of such systems. This paper proposes a new energy management system for isolated microgrids, which considers the voltage stability and generation contingency constraints.A multi-objective security constrained microgrid energy management system (MOSC-MEMS) based on a coordinated unit commitment-optimal power flow (UC-OPF) framework is introduced, aiming at minimization of real power losses, voltage deviations, and power generation costs. A Pareto optimal front is obtained using the -constraint method, and the best compromise solution is derived using the Fuzzy Satisfying criterion. Furthermore, a CIGRE benchmark test system based on European medium voltage network is used to demonstrate the performance of the proposed MOSC-MEMS. The simulation results, obtained by implementing the proposed framework in general algebraic modeling system (GAMS), indicate the feasibility of the proposed model and imply the significance of security constraints in the microgrid energy management systems
مقاله انگلیسی
4 Residential microgrid energy management considering flexibility services opportunities and forecast uncertainties
مدیریت انرژی ریز شبکه مسکونی با توجه به فرصتهای خدمات انعطاف پذیری و پیش بینی عدم قطعیت-2020
In the context of smart cities, the growing share of solar power induces uncertainty in power generation due to inherent climatic variations. Accurate forecasting will be a key point for future residential microgrids since inability to do so could dramatically impact power balance and grid stability. This paper proposes an enhanced energy management framework which aims to efficiently address uncertainty issues due to local climatic variations in a peninsula context. The proposed framework uses a ten-state Markov chain to generate stochastic solar irradiation as well as a forecast correction method based on recursive least-squares updated every hours in order to efficiently take part in hour-ahead power bidding process. Numerical results highlight benefits obtained by combining proposed forecast correction method and storage in a practical example of Saint-Nazaire, a city located in peninsula of Guérande, France. Besides, a sensitivity analysis regarding impact of storage size and aggregator penalty on operation cost and commitment indices is investigated. Obtained results demonstrates better accuracy in delivering power to the grid and will lead residential microgrids dealing with strong climatic variations to decrease their operation cost and increase power balance for all grid stakeholders.
Keywords: Residential microgrid | Uncertainties | Weather forecast | Markov chain | Recursive least squares | Grid services | Energy storage system
مقاله انگلیسی
5 Decentralized multi-agent based energy management of microgrid using reinforcement learning
مدیریت انرژی مبتنی بر چند عامل غیرمتمرکز بر روی ریز شبکه با استفاده از یادگیری تقویتی-2020
This paper proposes a multi-agent based decentralized energy management approach in a grid-connected microgrid (MG). The MG comprises of wind and photovoltaic resources, diesel generator, electrical energy storage, and combined heat and power generations to serve electrical and thermal loads at the lower-level of energy management system (EMS). All distributed energy resources (DERs) and customers are modelled as self-interested agents who adopt reinforcement learning to optimize their behaviours and operation costs. Based on this algorithm, agents have the capability to interact with each other in a distributed manner and find the best strategy in competitive environment. At the upper-level of EMS, there is an energy management agent that gathers the information of agents of lower-level and clears the MG electrical and thermal energy market in line with predetermined goals. Utilizing energy availability from different DERs and variety of customers’ consumption patterns, considering uncertainty of renewable generation and load consumption and taking into account technical constraint of DERs are the strengths of the presented framework. Performance of the proposed algorithm is investigated under different conditions of agents learning and using ε-greedy, soft-max and upper confidence bound methods. The simulation results verify efficacy of the proposed approach.
Keywords: Distributed energy resources | Microgrid energy management system | Multi-agent systems | Reinforcement learning
مقاله انگلیسی
6 Reducing the computational burden of a microgrid energy management system
کاهش بار محاسباتی یک سیستم مدیریت انرژی ریز شبکه-2020
As renewable technology advances and decreases in cost, microgrids are becoming an appealing means of distributed generation both for isolated communities and integrated with existing electrical grid systems. Due to their small size, however, microgrids may have financial limitations which preclude them from using commercial software to optimize control of their assets. Open-source optimization solvers are a viable alternative, but increase computation time. This work expands on a rolling horizon optimization framework for economic dispatch within an existing residential microgrid located in Hoover, Alabama. The microgrid has an open-source solver requirement and a need for quick solution time on a rolling horizon as opposed to a day-ahead commitment. We present a method of reducing integer variables by relaxation which completes two goals: reduction in computation time for real-time operations, and reduction in daily operational cost for the microgrid. Seasonal data for load and photovoltaic (PV) power was also collected from the microgrid to facilitate simulation testing. Computation time was successfully reduced using multiple variations of the relaxation method, while obtaining solution quality with operational cost similar to or better than the original model.
Keywords: Microgrids | Mixed-integer programming | Energy management system | Rolling horizon
مقاله انگلیسی
7 A microgrid energy management system based on chance-constrained stochastic optimization and big data analytics
یک سیستم مدیریت انرژی ریز شبکه مبتنی بر بهینه سازی تصادفی محدود شده و تحلیل داده های بزرگ-2020
A Microgrid (MG) is a promising distributed technology to solve todays energy challenges. They are changing how electricity is produced, transmitted, and distributed, enabling to capture massive amounts of data from sensors, and other electrical infrastructures. However, recent advances in modeling and optimization of MG neither integrate the use of big data technologies aggressively nor focus on developing an optimal operational strategy for a single building. To bridge this gap, this research proposes to use Apache Spark to enhance the performance of a scalable stochastic optimization model for an MG for multiple buildings, and to ensure that a significant portion of the wind power output will be utilized. The decision model is formulated as a chance constraint two-stage optimization problem to obtain operation decisions for a behind-the-meter topology. The comparison between the current practice of using historical data and integrating Apache Spark technologies demonstrates the superiority of the streaming data as energy management strategy. Experiments under different settings show that using big data strategy, the model can (1) achieve more cost savings of the total system, (2) increase resiliency to power disturbances, and (3) build a data analytics framework to enhance the decisionmaking process.
Keywords: Sustainability | Microgrid | Big data | Spark streaming | Stochastic optimization | Wind power
مقاله انگلیسی
8 Multi-agent microgrid energy management based on deep learning forecaster
مدیریت انرژی میکروگیدر چند عامل مبتنی بر پیشگویی یادگیری عمیق-2019
This paper presents a multi-agent day-ahead microgrid energy management framework. The objective is to minimize energy loss and operation cost of agents, including conventional distributed generators, wind turbines, photovoltaics, demands, battery storage systems, and microgrids aggregator agent. To forecast market prices, wind generation, solar generation, and load demand, a deep learning-based approach is designed based on a combination of convolutional neural networks and gated recurrent unit. Each agent utilizes the designed learning approach and its own historical data to forecast its required parameters/data for scheduling purposes. To preserve the information privacy of agents, the alternating direction method of multipliers (ADMM) is utilized to find the optimal operating point of microgrid distributedly. To enhance the convergence performance of the distributed algorithm, an accelerated ADMM is presented based on the concept of over-relaxation. In the proposed framework, the agents do not need to share with other parties either their historical data for forecasting purposes or commercially sensitive information for scheduling purposes. The proposed framework is tested on a realistic test system. The forecast values obtained by the proposed forecasting method are compared with several other methods and the accelerated distributed algorithm is compared with the standard ADMM and analytical target cascading.
Keywords: Microgrid energy management system | Short-term forecasting | Deep learning | Convolutional neural networks | Gated recurrent unit | Alternating direction method of multipliers
مقاله انگلیسی
9 Game-theoretical Energy Management for Energy Internet with Big Data-based Renewable Power Forecasting
مدیریت انرژی بازی تئوری برای اینترنت انرژی با پیش بینی قدرت قابل بازیافت مبتنی بر داده های بزرگ-2017
Energy internet, as a major trend in power system, can provide an open framework for integrating equipments of energy generation, transmission, storage and consumption, etc., so that global energy can be managed and controlled efficiently by information and communication technologies. In this paper, we focus on the coordinated management of renewable and traditional energy, which is a typical issue on energy connections. We consider a conventional power system consisting of the utility company, the energy storage company, the microgrid, and electricity users. Firstly, we formulate the energy management problem as a three-stage Stackelberg game, and every player in the electricity market aims to maximize its individual payoff while guaranteeing the system reliability and satisfying users’ electricity demands. We employ the backward induction method to solve the three-stage non-cooperative game problem, and give the closed-form expressions of the optimal strategies for each stage. Next, we study the big data-based power generation forecasting techniques, and introduce a scheme of the wind power forecasting, which can assist the microgrid to make strategies. Furthermore, we prove the properties of the proposed energy management algorithm including the existence and uniqueness of Nash equilibrium and Stackelberg equilibrium. Simulation results show that accurate prediction results of wind power is conducive to better energy management.
Index Terms: energy internet | Stackelberg game | microgrid energy management | wind power forecasting.
مقاله انگلیسی
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