Energy Management Problem Of Microgrids Research Articles

A multi-objective energy scheduling of the reconfigurable off-grid microgrid with electric vehicles using demand response program

Microgrids are essential in ensuring responsible energy consumption and production patterns to support and advance the United Nations’ sustainable development goals. However, due to the limited supply in the off-grid microgrids, an increase in the integration of electric vehicles (EVs) into electrical networks for their charging operation is expected to create a void between the energy supply and demand. Demand response (DR) programs provide a better solution to the energy management problem of microgrids. However, the difference in the objectives of the stakeholders of the off-grid microgrid can create an issue in providing optimal solutions to its energy scheduling problem. This paper focuses on the multi-objective energy scheduling strategy of the off-grid microgrid to manage its limited energy supply optimally using an incentive-based DR program. A fast heuristic approach is also presented in this work to reconfigure off-grid microgrid optimally during its hourly operations. Furthermore, Hong’s (2m+1) point estimation method is used in this work to consider the uncertainties in the EVs and other DR participants. The proposed work is analyzed on the 33-bus and 69-bus off-grid microgrid consisting of droop-controlled distributed generations, EV charging stations, and consumers with curtailable loads. For the day-ahead operation of the off-grid microgrids, the analysis shows that the proposed work reduces the operational cost of the two off-grid microgrids by 2.3% and 4.56%, respectively.

Reinforcement learning for energy management of an islanded microgrid: Analysis of state, state space and reinforcement function

The energy management (EM) of a microgrid is a challenging problem because of the stochasticity of renewable sources and loads, which can be formulated as a multi-stage decision-making problem. Reinforcement learning (RL), is found to be suitable for solving multi-stage decision-making problems under uncertainty. With the recent advancements in deferrable types of loads like that of electric vehicles, there is flexibility to manage peak demand, thereby saving the microgrid's operation cost. In this paper, we systematically explain for the first time how RL can be used to solve the scheduling problem of an isolated MG. A novel Q-learning algorithm employing a state-dependent action set is proposed to solve an isolated microgrid's EM problem in the presence of critical and deferrable loads. Various scenarios of wind turbine output power are generated to test the algorithm under a stochastic environment. In this work, the action set is divided into two: one with all the actions included and the second with a reduced number of actions. The entire state space is also divided into two: one set in which the deferrable load needs to be allotted and other where it need not be allotted. The advantage of such a method is that it substantially reduces the computational burden even if the state space's dimension is higher. Also, the judicious selection of reinforcement function to meet all the objectives and constraints is illustrated with case studies. The proposed method yields optimized scheduling with significant cost reductions compared to existing work.

Renewable energy effects on energy management based on demand response in microgrids environment

With further penetration of low-carbon energy conversion, microgrids (MGs) have become a necessary tool for expanding the consumption of renewable energies. In this paper, an optimal operation model for a microgrid-based multi-agent system is proposed. The goal is to save the total energy cost, which is expressed as a sum of locally observable convex functions. Therefore, improving the operational efficiency of microgrids is the key to promoting renewable energy development. This paper develops a three-layer multi-agent system model considering energy storage system and power thermal load demand response to solve the energy management problem of microgrids. In order to investigate the effect of energy storage system and demand response in microgrids, this paper designs three simulation cases, namely infrastructure case, energy storage case and demand response case. In order to prove the effectiveness of the proposed method, this paper uses the proposed method to solve three cases and compare the result with other meta-heuristic algorithms. The comparison results show that: (1) the multi-agent system model can realize the joint optimization of "resource, network, load and storage". (2) The introduction of energy storage and demand response system in microgrids can stabilize the output. Renewable energy units promote the use of renewable energy and reduce the overall operating cost of microgrids. Moreover, the results clearly demonstrate that the proposed algorithm has far better performance than other optimization methods. Also, the analysis obtained from the results shows that the cost is reduced by 1.82%. PV and WT output increased by 14.54% and 2.42%. In addition, their standard deviation decreases after ESS participation. The proposed approach is very effective through a simulation case study, which shows high potential for applications.

Deep reinforcement learning for real-time economic energy management of microgrid system considering uncertainties

The electric power grid is changing from a traditional power system to a modern, smart, and integrated power system. Microgrids (MGs) play a vital role in combining distributed renewable energy resources (RESs) with traditional electric power systems. Intermittency, randomness, and volatility constitute the disadvantages of distributed RESs. MGs with high penetrations of renewable energy and random load demand cannot ignore these uncertainties, making it difficult to operate them effectively and economically. To realize the optimal scheduling of MGs, a real-time economic energy management strategy based on deep reinforcement learning (DRL) is proposed in this paper. Different from traditional model-based approaches, this strategy is learning based, and it has no requirements for an explicit model of uncertainty. Taking into account the uncertainties in RESs, load demand, and electricity prices, we formulate a Markov decision process for the real-time economic energy management problem of MGs. The objective is to minimize the daily operating cost of the system by scheduling controllable distributed generators and energy storage systems. In this paper, a deep deterministic policy gradient (DDPG) is introduced as a method for resolving the Markov decision process. The DDPG is a novel policy-based DRL approach with continuous state and action spaces. The DDPG is trained to learn the characteristics of uncertainties of the load, RES output, and electricity price using historical data from real power systems. The effectiveness of the proposed approach is validated through the designed simulation experiments. In the second experiment of our designed simulation, the proposed DRL method is compared to DQN, SAC, PPO, and MPC methods, and it is able to reduce the operating costs by 29.59%, 17.39%, 6.36%, and 9.55% on the June test set and 30.96%, 18.34%, 5.73%, and 10.16% on the November test set, respectively. The numerical results validate the practical value of the proposed DRL algorithm in addressing economic operation issues in MGs, as it demonstrates the algorithm’s ability to effectively leverage the energy storage system to reduce the operating costs across a range of scenarios.

Network-aware energy management for microgrids in distribution market: A leader-followers approach

With the increasing penetration of distributed energy resources (DERs) into distribution systems, microgrids can participate in the market clearing of distribution-level electricity market. This paper proposes a leader-followers-type bi-level model for energy management and market clearing of distribution system with microgrids based on Stackelberg game. Distribution system operator (DSO) is considered as the leader, and microgrid operators (MOs) as followers. In the proposed model, market clearing problem of distribution system is modeled in the upper level, while the energy management problem of microgrids is modeled in the lower level. The distribution locational marginal price (DLMP) is obtained in the upper level model and then transmitted to the lower level model as price signals for microgrids. The model is then reformulated as a Mathematical Program with Equilibrium Constraints (MPEC) problem and the stochastic MPEC model is constructed with the uncertainty of PVs in microgrids. Existence of equilibrium of the proposed single leader multi-follower game is analyzed. Numerical results demonstrate the effectiveness of the proposed model.

A chaotic sine cosine algorithm with crossover operator for microgrid energy scheduling considering uncertainty

Microgrid (MG) systems are growing at a rapid pace since they can accommodate the high amount of renewable energy. Since the MG consists of small distributed generators (DG) with volatile characteristics, an efficient energy management system is the main requisite in MG. In this paper, a chaotic sine cosine algorithm with crossover operator (CSCAC) is proposed for the day-ahead MG optimal energy scheduling problem. CSCAC includes a novel non-linear transition parameter based on the chaos system which can help the algorithm escape from local optima. A chaotic search operator is proposed to enhance the local search ability. Furthermore, a crossover operator is devised to combine the advantages of different search strategies and achieve a comparatively better balance of exploration and exploitation. First, the effectiveness of CSCAC is validated on several benchmark functions. Then, it is applied to the day-ahead energy scheduling in a MG with three wind power plants, two photovoltaic power plants and a combined heat and power plant (CHP). Furthermore, it is implemented in two more cases considering the uncertainty and stochastic nature of the renewable power sources. Experimental results demonstrate the superiority of CSCAC over other comparative algorithms in the optimal MG energy management problem.

Analytic Hierarchy Process (AHP) – Swarm intelligence based flexible demand response management of grid-connected microgrid

The integration of demand response programs (DRP) into the microgrid energy management system can enhance the load characteristics by enabling the active participation of consumers to achieve techno-economic benefits. From the microgrid operator's perspective, these DRP can optimize the revenue and accelerate return on investment. The previous research studies do not accurately represent modelling load responsiveness and price elasticities while implementing demand response programs. The accurate assessment of consumer behaviour to market price changes is paramount while modelling the price elasticities. With this viewpoint, a new microgrid energy management problem is proposed with the integration of flexible price elasticity based incentive-driven DRP in this research work. Further, the network flow constraints are considered to evaluate power losses and voltage deviation at each node of the modified IEEE-33 and IEEE-38 bus distribution networks. The comprehensive load model with the combination of linear and nonlinear conservative load responsive models is adopted and derived for each incentive-driven DRP by considering the nonlinear incentive and penalty model. Nine possible demand response scenarios were created, and techno-economic performance indices for each scenario were evaluated. Finally, the Fuzzy Analytic Hierarchy Program is adopted to choose the best alternative based on each techno-economic criterion. The recently reported swarm optimizer Sparrow Search Algorithm is devised for the first time in the microgrid literature to solve the above-proposed microgrid energy management problem. The obtained results are compared with existing metaheuristics to test the algorithm performance.

Energy management in microgrid using incentive-based demand response and reconfigured network considering uncertainties in renewable energy sources

Demand response (DR) programs and reconfiguration of distribution networks are generally adopted in the energy management (EM) problem of microgrid to enhance the technical and economical features of microgrid. Assuming a fixed configuration of distribution network, DR programs usually optimize the generation cost by encouraging the consumers to reduce their energy demands. Whereas reconfiguration of network is done for a pre-defined generation schedule and energy demand. However, separate incorporation of these two operational techniques in the EM problem may lead to a non-optimal solution. In this paper, a joint framework is proposed to integrate a novel incentive-based DR program and reconfiguration method in the EM problem of microgrid on a day-ahead time frame. The objective of the work is to minimize the fuel cost of conventional distributed generation (DG) and the cost of power purchased from the grid, while maximizing the profit for microgrid operator (MGO). The efficacy of the proposed model is tested on a static model of grid-connected 33-bus microgrid which consists of renewable energy (RE) sources and a conventional DG. To account the uncertainties in RE sources, Hong’s (2m+1) point estimation method (PEM) is considered in this work. The result confirms that the incorporation of DR program and reconfiguration method in the EM problem leads to an optimum energy schedule for the microgrid with a minimum lossy network. For the single-day operation of microgrid, it has been found that the power transfer from the grid and power lost in the network is reduced by 10.83% and 34.03% respectively.

Probabilistic Modeling and Equilibrium Optimizer Solving for Energy Management of Renewable Micro-Grids Incorporating Storage Devices

Recently, micro-grids (MGs) have had a great impact on power system issues due to their clear environmental and economic advantages. This paper proposes an equilibrium optimizer (EO) technique for solving the energy management problem of MGs incorporating energy storage devices concerning the emissions from renewable energy sources (RES) of MGs. Because of the imprecision and uncertainties related to the RESs, market prices, and forecast load demand, the optimization problem is described in a probabilistic manner using a 2m + 1 point estimation approach. Then, the EO approach is utilized for solving the probabilistic energy management (EM) problem. The EM problem is described according to the market policy on the basis of minimizing the total operating cost and emission from RESs through optimal settings of the power generated from distributed generators (DGs) and grids connected under the condition of satisfying the operational constraints of the system. The proposed EO is evaluated based on a grid-connected MG that includes energy storage devices. Moreover, to prove the effectiveness of the EO, it is compared with other recently meta-heuristic techniques. The simulation results show acceptable robustness of the EO for solving the EM problem as compared to other techniques.

Knee Point-Guided Multiobjective Optimization Algorithm for Microgrid Dynamic Energy Management

Model predictive control (MPC) technology can effectively reduce the bad effect caused by inaccurate data prediction in microgrid energy management problem. However, the use of MPC technology needs to dynamically select an optimal solution from the Pareto solution set to implement, which needs the participant of the decision-makers frequently. In order to reduce the burden on decision-makers, we designed a knee point-based evolutionary multiobjective optimization algorithm, termed KBEMO. Knee point is the solution on Pareto front with the maximum marginal utility, which is considered as the preferred solution if there is no other preference. This algorithm focuses on obtaining the knee region and automatically outputs knee points after the optimization. By combining this algorithm with MPC technology, it can effectively reduce the amount of computational consumption and obtain better convergence. Experimental results show that this method is more competitive than the traditional single-objective MPC method.

Review and clustering of optimal energy management problem studies for industrial microgrids

The optimal energy management problem of microgrids is an agenda item for the energy field. Especially industrial microgrids, whose energy demand is high and uninterruptible, need to be managed by a holistic view of optimization. Technical researches in this field are to be enriched with the literature review that looks through various angles. In the scope of this paper, the research literature is reviewed deeply and analyzed with the aim of presenting the gaps. Articles written in English, in the period of 2010 to 2019 have been revised thoroughly. By eliminating the search results, 105 relevant articles have been determined. Objective(s), criteria for analysis, processes included, power source(s), storage system, other system components and methodology of each article have been studied. The literature has been summarized, it has been seen that solar, wind, and diesel sources are used in about 82%, 55%, and 16% of articles, respectively. Then, they have been clustered using self-organizing maps. Nine clusters have been obtained which separate the articles clearly. Especially, three of them which have fewer data have been analyzed. Clustering results have been interpreted and utilized to show the research openings. Achievements of this study will lead the scientific research that will fill in the gaps.

RETRACTED ARTICLE: Multi-objective energy management in microgrids with hybrid energy sources and battery energy storage systems

Microgrid with hybrid renewable energy sources is a promising solution where the distribution network expansion is unfeasible or not economical. Integration of renewable energy sources provides energy security, substantial cost savings and reduction in greenhouse gas emissions, enabling nation to meet emission targets. Microgrid energy management is a challenging task for microgrid operator (MGO) for optimal energy utilization in microgrid with penetration of renewable energy sources, energy storage devices and demand response. In this paper, optimal energy dispatch strategy is established for grid connected and standalone microgrids integrated with photovoltaic (PV), wind turbine (WT), fuel cell (FC), micro turbine (MT), diesel generator (DG) and battery energy storage system (ESS). Techno-economic benefits are demonstrated for the hybrid power system. So far, microgrid energy management problem has been addressed with the aim of minimizing operating cost only. However, the issues of power losses and environment i.e., emission-related objectives need to be addressed for effective energy management of microgrid system. In this paper, microgrid energy management (MGEM) is formulated as mixed-integer linear programming and a new multi-objective solution is proposed for MGEM along with demand response program. Demand response is included in the optimization problem to demonstrate it’s impact on optimal energy dispatch and techno-commercial benefits. Fuzzy interface has been developed for optimal scheduling of ESS. Simulation results are obtained for the optimal capacity of PV, WT, DG, MT, FC, converter, BES, charging/discharging scheduling, state of charge of battery, power exchange with grid, annual net present cost, cost of energy, initial cost, operational cost, fuel cost and penalty of greenhouse gases emissions. The results show that CO2 emissions in standalone hybrid microgrid system is reduced by 51.60% compared to traditional system with grid only. Simulation results obtained with the proposed method is compared with various evolutionary algorithms to verify it’s effectiveness.

A hybrid prediction-based microgrid energy management strategy considering demand-side response and data interruption

Data interruption may cause the centralized energy management system of a microgrid (MG) to collapse. To solve this problem, a hybrid prediction-based energy management strategy is proposed in this paper to predict interrupted data for the centralized dispatching process of an MG. This hybrid prediction method is designed following a combination of model predictive control and extreme learning machine techniques. Based on the predicted data of distributed energy resources and three types of loads from demand-side response, an optimization model is formulated to minimize the operational cost. If some predicted data are interrupted during transmission, then a prospect vulnerability assessment method is applied to select a neighboring device to predict the interrupted data. In the end, an improved particle swarm optimization algorithm is proposed with the help of genetic algorithms to accelerate convergence to global optimal solutions for the proposed MG energy management problem. The effectiveness of the proposed models and solution methods is also verified by a case study.

Continuous optimal control approaches to microgrid energy management

We propose a novel method for the microgrid energy management problem by introducing a nonlinear, continuous-time, rolling horizon formulation. The method is linearization-free and gives a global optimal solution with closed loop controls. It allows for the modelling of switches. We formulate the energy management problem as a deterministic optimal control problem (OCP). We solve (OCP) with two classical approaches: the direct method and Bellman’s Dynamic Programming Principle (DPP). In both cases we use the optimal control toolbox Bocop for the numerical simulations. For the DPP approach we implement a semi-Lagrangian scheme adapted to handle the optimization of switching times for the on/off modes of the diesel generator. The DPP approach allows for accurate modelling and is computationally cheap. It finds the global optimum in less than one second, a CPU time similar to the time needed with a Mixed Integer Linear Programming approach used in previous works. We achieve this result by introducing a ‘trick’ based on the Pontryagin Maximum Principle. The trick reduces the computation time by several orders and improves the precision of the solution. For validation purposes, we performed simulations on datasets from an actual isolated microgrid located in northern Chile. The result shows that the DPP method is very well suited for this type of problem.

Optimal dispatch for a microgrid incorporating renewables and demand response

This paper proposes an optimal economic dispatch of a grid connected microgrid. The microgrid consists of solar photovoltaic, diesel and wind power sources. An Incentive Based Demand Response Program is incorporated into the operations of the grid connected microgrid. The optimal dispatch strategy is obtained by minimizing the conventional generators fuel cost, the transaction costs of the transferable power and maximizing the microgrid operator's demand response benefit whilst simultaneously satisfying the load demand constraints amongst other constraints. The developed mathematical model is tested on two practical case studies and sensitivity analysis of the model to key parameters was also performed. Case study 1 consists of three conventional generator units, one wind generator, one solar generator and three rural customers. Case study 2 is a much larger microgrid and was chosen to test the applicability of our model to larger microgrids and also to verify the scalability of our algorithm. Results show that the demand response program curtails significant grid relieving amounts of energy in the two case studies considered and integration of an incentive based demand response programs into the microgrid energy management problem introduces optimality at both the supply and demand spectrum of the grid.

Lithium‐ion battery modelling for the energy management problem of microgrids

This study presents a mathematical model of lithium-ion (Li-ion) batteries in the energy management (EM) problem of a microgrid (MG). In this study, the authors develop a detailed model of Li-ion batteries that considers the degradation cost associated with operation, controllable and uncontrollable charging ramps, other limits, and the operating characteristics provided by the manufactures. The Li-ion battery degradation cost is analysed using different approaches and is compared with modelling without this cost, using a quadratic degradation cost, and using a piecewise degradation cost. Furthermore, this cost is analysed using a linear cost that takes the life expectancy based on the number of cycles of the battery into account. To analyse the proposed method and other modelling approaches, the authors examine the battery model in an EM problem in an MG. This MG, which can be connected to the main grid, also uses wind and photovoltaic as generation resources, in addition to a backup generator. The EM problem is modelled as a deterministic mixed-integer linear (or quadratic) problem; the results of eleven different cases are used in the analysis of the proposed Li-ion battery model for a 24 h planning horizon.

A mixed integer linear programming model for the energy management problem of microgrids

This paper presents a mathematical model for the energy management (EM) problem of a microgrid (MG) by means of a mixed integer linear programming approach. In the EM problem, the objective is to determine a generation and a controllable load demand policy that minimises, over a planning horizon, the operation cost subject to economical and technical constraints. We propose a detail modelling for microturbines (MTs) and fuel cells (FCs), where the constraints associated with such factors as the ramps, minimum up and downtime, and generation limits, represent various peculiarities that have not been adequately considered in literature. The proposed model also considers a detailed representation of critical, reschedulable and curtailable loads, which are important aspects in the MG concept. To analyse the proposed modelling, a MG is used along with a MT, a FC, a battery bank, wind and photovoltaic generators connected to the main grid. The results indicate that the model is adequate for the MG EM.

On a fault tolerant strategy for efficient energy management in microgrid systems

This paper addresses the microgrid energy management problem within a coherent framework of control tools based on Mixed-Integer Linear Programming (MILP) and constrained Model Predictive Control (MPC). These help characterize the microgrid components’ dynamics and the overall system control architecture. A fault tolerant strategy is considered in order to ensure the proper amount of energy in the storage devices such that (together with the utility grid) the essential consumer demand is reliably covered. Simulation results on a particular microgrid architecture validate the proposed approach.

A Centralized Energy Management System for Isolated Microgrids

This paper presents the mathematical formulation of the microgrid's energy management problem and its implementation in a centralized Energy Management System (EMS) for isolated microgrids. Using the model predictive control technique, the optimal operation of the microgrid is determined using an extended horizon of evaluation and recourse, which allows a proper dispatch of the energy storage units. The energy management problem is decomposed into Unit Commitment (UC) and Optimal Power Flow (OPF) problems in order to avoid a mixed-integer non-linear formulation. The microgrid is modeled as a three-phase unbalanced system with presence of both dispatchable and non-dispatchable distributed generation. The proposed EMS is tested in an isolated microgrid based on a CIGRE medium-voltage benchmark system. Results justify the need for detailed three-phase models of the microgrid in order to properly account for voltage limits and procure reactive power support.