Optimal Energy Management Of Microgrid Research Articles

Optimal Economic Dispatch and Power Generation for Microgrid Using Novel Lagrange Multipliers-Based Method With HIL Verification

Microgrid incorporating distributed renewable energy resources (RERs) is increasingly important owing to the goal to reach net-zero emissions by mid-century. This article deals with the optimal energy management of microgrid comprising RERs and battery energy storage system. In addition to considering power flow constraints and uncertainties of RERs, the importance of retaining profits of microgrid operators and the needs for providing extra supports to the main grid are rising. To meet all the requirements, a novel Lagrange multipliers-based method is proposed to deal with equality and inequality constraints simultaneously to directly obtain the optimal economic dispatch solution analytically. Moreover, a microgrid built in Cimei Island of Penghu Archipelago, Taiwan, is investigated to examine the compliance with the requirements of equality and inequality constraints and the performance of the Lagrange multipliers-based method. Furthermore, the comparison of the proposed method with experience-based energy management system, Newton-particle swarm optimization, and deep Q-learning network is provided to evaluate the obtained solutions. Finally, through the hardware in the loop mechanism, which is built using OPAL-RT real-time simulator with floating-point digital signal processor, the effectiveness of proposed Lagrange multipliers-based method is verified and proven to be pragmatic.

Hybrid model predictive control of renewable microgrids and seasonal hydrogen storage

Optimal energy management of microgrids enables efficient integration of renewable energies by considering all system flexibilities. For systems with significant seasonal imbalance between energy production and demand, it may be necessary to integrate seasonal storage in order to achieve fully decarbonized operation. This paper develops a novel model predictive control strategy for a renewable microgrid with seasonal hydrogen storage. The strategy relies on data-based prediction of the energy production and consumption of an industrial power plant and finds optimized energy flows using a digital twin optimizer. To enable seasonal operation, incentives for long-term energy shifts are provided by assigning a cost value to the storage charge and adding it to the optimization target function. A hybrid control scheme based on rule-based heuristics compensates for imperfect predictions. With only 6% oversizing compared to the optimal system layout, the strategy manages to deliver enough energy to meet all demand while achieving balanced hydrogen production and consumption throughout the year.

Model-free data-driven approach assisted Deep Reinforcement Learning for Optimal Energy Management in MicroGrid

Uncertainties of solar PhotoVoltaic (solar PV) generation and Electric Vehicle (EV) demand are major issues for Optimal Energy Management (OEM) tasks in MicroGrid (MG), especially regarding power system stability and increased overall demand. A Probabilistic Load Flow (PLF) with a Battery Energy Storage System (BESS) controlled by an Energy Management System (EMS) can deal with these issues. However, the PLF and optimization algorithm based on the iterative method leads to a high computation burden. Therefore, this paper proposes a model-free data-driven approach assisted Deep Reinforcement Learning (DRL) to decrease the computation burden of PLF and problem-solving. Deep Neural Networks (DNNs) are developed as a model-free data-driven to estimate the power flow parameters of MG instead of PLF. Moreover, the DRL named a Deep Deterministic Policy Gradient (DDPG) is deployed as the optimization algorithm to find the optimal solution in the OEM task. In addition, finding appropriate parameters of the DDPG is proposed in this paper. To showcase the efficacy of the proposed method, a low-voltage distribution network is developed as the MG. Objective functions including exchanged energy, carbon emission, and BESS degradation costs are considered in this work. Simulation results indicate that the proposed method is capable of reducing the computation burden by 88.47% in comparison to the Differential Evolution (DE) algorithm that uses 10 populations. Moreover, the best agent model obtained from finding suitable parameters of DDPG can provide a total cost of 115.63 USD/day, which is the lowest cost compared with the cost obtained by the DE.

Optimal energy management of microgrid based wind/PV/diesel with integration of incentive-based demand response program

The combination of demand response as demand side management together with energy management system has become essential to minimize energy cost, to maintain continuous supply of electricity, and to improve the safety of power system operation. This paper studies the optimal energy dispatch of connected microgrid units containing photovoltaic panels, wind turbine generators, diesel generators, and the main grid. The optimal set point of microgrid’s units is determined to satisfy the required load demand for a day-ahead horizon time. As the demand response is an important way of demand side management, this paper proposes as the main contribution the implementation of demand response cost as one of the objective functions to be maximized to view its effect on load demand consumption, on MG energy production and on MG energy cost. The demand response is implemented by using an incentive based demand response program in the optimization model in addition to the fuel cost of diesel generators and the transfer cost of transferable power. The incentive payment offered by utilities is used to motivate consumers to change their energy consumption behavior and thus to reduce their power consumption and maintain the system reliability during on-peak periods. Thus the objective function is formulated to maximize microgrid operator’s demand response benefit, and to minimize both the fuel cost of diesel generators, and the transfer cost of transferable power. For this purpose, the defined objective function is solved by a Hybrid Particle Swarm Optimization with Sine Cosine Acceleration Coefficients (H-PSO-SCAC) algorithm for an optimal energy management system of the connected microgrid. For the simulation tests, different algorithms are examined in order to validate the effectiveness of the H-PSO-SCAC algorithm. The impact of demand response program is analyzed on the load demand consumption, on the microgrid energy production and its influence on the optimized microgrid cost function. The results demonstrate that the implementation of demand response has changed the previous situation that costumers do not participate in the operation of the power system. And it enables microgrid to decrease load consumption, microgrid energy production, as well as energy cost.

Optimal energy management of micro-grid using sparrow search algorithm

In this paper, a recent metaheuristic approach of sparrow search algorithm (SSA) is employed to manage the operation of microgrid (MG) in an optimal manner. Two optimization problems are formulated in this work, the first one is a single objective problem that aims at minimizing either the total operating cost or the total emission from the system. The second problem is multi-objective comprising both total operating cost and total emission simultaneously. A new version of SSA is proposed to manage the energy of MG optimally. The considered MG comprises photovoltaic modules (PV), wind turbine (WT), fuel cell (FC), micro-turbine (MT), batteries (BSS), and grid. The obtained results using the proposed SSA are compared to other programmed approaches of krill herd optimizer (KH), Harris hawks optimizer (HHO), artificial ecosystem-based optimizer (AEO), Fuzzy-self adaptive particle swarm optimization (FSAPSO), and antlion optimizer (ALO). Non-parametric analysis using Friedman and Kruskal–Wallis ANOVA tests are applied for evaluating the proposed algorithm performance statistically. At solving the single objective problem, the proposed SSA achieved minimizing in cost and emission with 1.44% and 54.76% respectively compared to KH. Regarding the multi-objective problem, the proposed multi-objective SSA (MOSSA) succeeded in saving about 42.78% in operating cost and reducing the emission by 0.118% compared to ALO. The main findings demonstrated the robustness of the proposed SSA in managing the operation of the constructed MG.

Optimal Energy Management of Microgrids Using Quantum Teaching Learning Based Algorithm

Quantum inspired computational intelligence is gaining momentum in the interest of enhancing the performance of existing metaheuristic optimization while solving multi-dimensional nonlinear problems. The microgrid optimal energy scheduling is one such problem that involves multiple distributed energy resources (DER) with volatile characteristics and proficient energy management is essential for their coordination and reducing global carbon emissions. Relatively very few works in the existing literature have attempted to solve this problem using quantum-based algorithms. In this article, a stochastic framework associated with the Quantum Teaching Learning-based optimization (QTLBO) algorithm is devised for the first time to optimize energy flow in the microgrids. Four scenarios concerning seasonal variations are chosen to address the uncertainties related to generated power from DERs with better accuracy. The day-ahead optimum power scheduling configuration of DERs is evaluated for each scenario. The performance of QTLBO is assessed on a grid-connected microgrid network and compared with existing metaheuristic algorithms such as the Real-coded Genetic Algorithm, Differential Evolution, and TLBO. The obtained simulation results prove the superiority of QTLBO in terms of convergence and achieving a global optimum solution by overcoming premature convergence. Further, the proposed stochastic framework is helpful to attain techno-economic benefits to both customers and market operators.

Optimal energy management in microgrid including stationary and mobile storages based on minimum power loss and voltage deviation

Optimal energy management in microgrid including stationary and mobile storages based on minimum power loss and voltage deviation

Real-time optimal energy management of microgrid with uncertainties based on deep reinforcement learning

Microgrid (MG) is an effective way to integrate renewable energy into power system at the consumer side. In the MG, the energy management system (EMS) is necessary to be deployed to realize efficient utilization and stable operation. To help the EMS make optimal schedule decisions, we proposed a real-time dynamic optimal energy management (OEM) based on deep reinforcement learning (DRL) algorithm. Traditionally, the OEM problem is solved by mathematical programming (MP) or heuristic algorithms, which may lead to low computation accuracy or efficiency. While for the proposed DRL algorithm, the MG-OEM is formulated as a Markov decision process (MDP) considering environment uncertainties, and then solved by the PPO algorithm. The PPO is a novel policy-based DRL algorithm with continuous state and action spaces, which includes two phases: offline training and online operation. In the training process, the PPO can learn from historical data to capture the uncertainty characteristic of renewable energy generation and load consumption. Finally, the case study demonstrates the effectiveness and the computation efficiency of the proposed method.

Cloud-Fog Computing-Based Distributed Event-Triggered Consensus Predictive Compensation for Optimal Energy Management in Microgrid under DoS Attack

A cloud-fog computing-based event-triggered distributed energy optimization management method based on predictive attack compensation is proposed to address the problem of denial of service (DoS) attack, the complexity of computation, and the bandwidth constraint on the communication network in microgrids. Firstly, in order to optimize the energy supply of microgrid and maximize the profit, the minimum cost function of maintaining the balance of supply and demand is given considering the power loss of microgrid. Secondly, considering the problem of bandwidth-constrained communication, a distributed event-triggered consensus algorithm is proposed based on fog computing. Thirdly, a model predictive compensation algorithm based on cloud computing is proposed, which uses the mismatched power between supply and demand at the historical time before the attack to predict and compensate the missing data of the agent power at the current time and many times after attack. Finally, the effectiveness of the proposed method is verified by simulation results.

Multi-objective optimal energy management of microgrids including plug-in electric vehicles with the vehicle to grid capability for energy resources scheduling

As more battery electric vehicles and plug-in hybrid electric vehicles are connected to the microgrid, plug-in electric vehicles have a major impact on the microgrid. This paper proposes a multi-objective optimization energy management model including plug-in electric vehicles and other distributed generations. By analyzing the powertrain structure of different kinds of plug-in electric vehicles, the engine fuel consumption model, the charging model, the discharge model, and the battery state of charge model of plug-in electric vehicle in microgrids are given. The proposed model considers the plug-in electric vehicle battery state of charge constraints to prevent the battery from overcharging and over-discharging and gives the state of charge curve in microgrids. Simultaneously, an improved gray wolf algorithm, introducing optimization control factors and greedy strategies to better balance the mining and exploration capabilities of the gray wolf algorithm, is proposed to solve this multi-objective optimization energy management model. Compared with particle swarm optimization and traditional gray wolf algorithm, the improved algorithm further improves the accuracy and convergence speed. Besides, the improved algorithm is applied to three scheduling schemes, and the results show that plug-in hybrid electric vehicles have more advantages in energy economy in some special cases.

Optimal energy management of microgrids under environmental constraints using chaos enhanced differential evolution

Optimal energy management of microgrids is one of the challenging tasks in modern power systems. It helps to achieve maximum societal benefits in terms of economy and reduced environmental effects. Microgrid can be operated in grid connected as well as isolated mode. A novel hybrid optimization technique using differential evolution (DE) and chaos theory is presented in this paper for optimal operation of a microgrid comprising of both renewable and non-renewable energy sources. The reduction of pollutants is also considered because of integration of non-renewable energy sources with the microgrid. The motivation for the proposed hybrid algorithm is to avoid premature convergence and stagnation. The optimal operation is formulated as a bi-objective optimization problem considering minimization of operating cost and reduction of pollutants simultaneously over a 24-h scheduling horizon. It is a complex non-linear optimization problem under a set of equality as well as inequality constraints. A microgrid consisting of wind turbine (WT), photovoltaic (PV), micro turbine (MT) fuel cell (FC) and battery units as storage device is considered for the present study. The microgrid is assumed to be grid connected. The proposed algorithm is tested on two systems in order to verify its effectiveness and efficiency. Further, the results obtained by the proposed technique are validated by comparing the same obtained by other recent methods. It observed that the proposed technique is capable of producing superior results in terms of cost and pollution.

Optimal energy management of microgrid based on multi-parameter dynamic programming

With the wide application of microgrid system, fluctuation and randomness are the characteristics of distributed generation output. The traditional energy management system can’t meet the requirements to ensure the security and stability of the grid. The microgrid energy management is of great significance to the stable operation of power grid. In order to obtain higher economic benefits and pay less environmental costs, reasonable scheduling of various distributed power sources is able to achieve this goal. In this article, microgrid energy management including distributed generation is studied. The objective function includes the economic objective and the environmental objective. The model of energy management is considered as a multi-objectives and multi-parametric optimization problem. The multi-parameter dynamic programming is used to optimize the energy management of microgrid. Finally, the efficiency of the proposed method is examined by the simulation studies.

A bio-inspired approach for probabilistic energy management of micro-grid incorporating uncertainty in statistical cost estimation

This article presents a stochastic framework in probabilistic optimal energy management (OEM) of renewable-based microgrid (MG) incorporating uncertainty. OEM of MG is an optimization problem to optimize functional cost of energy associated with distributed generation and responsive loads. Effective coordination between power generation and consumption becomes an essential assignment in MG operation that should be done at a minimum cost and proper scheduling. Renewable energy, load demand and market bids are random which requires a computationally amenable robust uncertainty model to deal with its uncertainty effects. Hence, OEM problem has been formulated using k-point estimation method (k-PEM) based on moments of input random variable (IRV). Inherent randomness associated with renewable energy parameters ascends the MG problem to be stochastic which requires a powerful heuristic tool to obtain optimal operating schedules and cost estimation. Hence, modified glowworm swarm optimization (MOGSO), a bio-inspired algorithm based on swarm intelligence is proposed to solve optimization functions globally in both deterministic and probabilistic approaches. In order to visualize the effectiveness of proposed hybrid PEM-MOGSO method, the MG OEM problem is solved considering three different scenarios to scrutinize its robustness in manipulating optimal operating schedules and statistical cost estimates.

Optimal scheduling of a renewable based microgrid considering photovoltaic system and battery energy storage under uncertainty

This paper suggests a new energy management system for a grid-connected microgrid with various renewable energy resources including a photovoltaic (PV), wind turbine (WT), fuel cell (FC), micro turbine (MT) and battery energy storage system (BESS). For the PV system operating in the microgrid, an innovative mathematical modelling is presented. In this model, the effect of various irradiances in different days and seasons on day-ahead scheduling of the microgrid is evaluated. Moreover, the uncertainties in the output power of the PV system and WT, load demand forecasting error and grid bid changes for the optimal energy management of microgrid are modelled via a scenario-based technique. To cope with the optimal energy management of the grid-connected microgrid with a high degree of uncertainties, a modified bat algorithm (MBA) is employed. The proposed algorithm leads to a faster computation of the best location and more accurate result in comparison with the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. The simulation results demonstrate that the use of practical PV model in a real environment improve the accuracy of the energy management system and decreases the total operational cost of the grid-connected microgrid.

Optimal energy management of microgrid using advanced multi-objective particle swarm optimization

Purpose The purpose of this paper is to propose an optimal energy management (OEM) method using intelligent optimization techniques applied to implement an optimally hybrid heat and power isolated microgrid. The microgrid investigated combines renewable and conventional power generation. Design/methodology/approach Five bio-inspired optimization methods include an advanced proposed multi-objective particle swarm optimization (MOPSO) approach which is comparatively applied for OEM of the implemented microgrid with other bio-inspired optimization approaches via their comparative simulation results. Findings Optimal multi-objective solutions through Pareto front demonstrate that the advanced proposed MOPSO method performs quite better in comparison with other meta-heuristic optimization methods. Moreover, the proposed MOPSO is successfully applied to perform 24-h OEM microgrid. The simulation results also display the merits of the real time optimization along with the arbitrary of users’ selection as to satisfy their power requirement. Originality/value This paper focuses on the OEM of a designed microgrid using a newly proposed modified MOPSO algorithm. Optimal multi-objective solutions through Pareto front demonstrate that the advanced proposed MOPSO method performs quite better in comparison with other meta-heuristic optimization approaches.

Multi-Objective Optimal Dispatching for a Grid-Connected Micro-Grid Considering Wind Power Forecasting Probability

In recent years, a large number of wind power has been applied in the micro-grid (MG). Influenced by randomness characteristics of wind speed, the uncertainty in the power output of wind turbines imposes some safety and stability problems on the optimal energy management in MG. To address this problem, an expert energy management system (EEMS) considering wind power probability is developed in this study for optimal dispatching of a typical grid-connected MG. The EEMS composes of wind power probabilistic forecasting module, multi-objective optimization module and energy storage system (ESS) module. In the wind power forecasting module, wind power probabilistic forecasting based on complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and Gaussian process regression (GPR) is proposed in this study. To improve the forecasting results, CEEMDAN, an effective signal processing method, is employed to decompose the wind power data, then, the decomposed subseries are utilized as the inputs of GPR for probabilistic forecasting. A two-step solution methodology combining an efficient and effective improved multi-objective bat algorithm (IMOBA) with fuzzy set theory (FST) is put forward to solve the optimal dispatching problems. In the first step, IMOBA is developed to optimize the energy dispatching of EEMS by minimizing both economic cost and pollutant emissions simultaneously, and obtain a well-distributed set of Pareto optimal front (POF), then, FST is employed to identify the best compromise solutions from POF. Six operational scenarios of a typical grid-connected MG based one-POF-one-day and one-POF-one-hour dispatching schemes are constructed to investigate the effectiveness of the proposed strategy and provide more flexibility for decision makers. The results illustrate that EEMS can effectively schedule power generation and energy storage by considering economic cost and pollutant emission objectives simultaneously.

Robust Day-Ahead Operation Planning of Unbalanced Microgrids

The optimal energy management of microgrids in the face of uncertainty is a challenging task. It requires practical modeling of the microgrid along with an efficient decision-making tool to effectively improve the economical operation and power quality. This paper presents a robust optimization framework for the day-ahead operation planning of unbalanced three-phase microgrids. The operation planning problem is formulated in detail as a mixed-integer nonlinear programming model. This model includes the operational constraints pertaining to voltage unbalance, synchronous generator unbalance operation, and power unbalance at the point of common coupling. Linearization techniques are proposed to transform this problem into a mixed-integer linear programming model for which efficient solvers are available. Two-stage robust optimization is used to obtain a robust operation planning decision against realizations of uncertain renewable generation, real-time market price, and voltage-dependent load parameters. This problem is solved using the column and constraint generation algorithm. The IEEE 37-bus and a 60-bus feeders are used to validate the performance of the robust solution.

Modified imperialist competitive algorithm for environmental constrained energy management of microgrids

Optimal energy management of microgrids with different objectives such as operation cost, air pollution and wider use of renewable energy sources is investigated in many research works. In this paper, the effect of different operation modes of microgrids to solve environmental/economic dispatch problem is evaluated. Different modes of operation of microgrid include islanding operation, grid-connected and unilateral or bilateral exchange of power with main grid. The imperialist competitive algorithm (ICA) is one of the evolutionary algorithms that can model the competing imperialists to absorb colonies and uses it to find optimum solution. In this paper, a new method to improve the ICA algorithm is proposed in which the movement radius of the colonies toward the imperialist is adaptively adjusted in accordance with their position by means of adding direction and velocity parameters to the colonies. Thus, the colonies are attracted and repelled more intelligently by imperialists. The proposed method can create a trade-off between exploitation (i.e. local search) and exploration (i.e. global search) and prevents premature convergence while minimizing trapping in the local minima. Simulation results and comparison with other algorithms confirm high performance of the proposed algorithm.

Toward Optimal Energy Management of Microgrids via Robust Two-Stage Optimization

This paper considers energy management in a grid-connected microgrid which has multiple conventional generators (CGs), renewable generators and energy storage systems (ESSs). A robust two-stage optimization approach is presented to schedule the energy generation under uncertainties, aimed at minimizing the long-term average operating cost subject to realistic operational and service constraints. The first stage of optimization determines hourly unit commitment of the CGs via a day-ahead scheduling, and the second stage performs economic dispatch of the CGs, ESSs, and energy trading via a real-time scheduling. The combined solution meets the need of handling large uncertainties in the load demands and renewable generation, and provides an efficient solution under limited computational resource which approximately optimizes the long-term average operating cost while meeting the quality-of-service requirements. The performance of the proposed strategy is evaluated by simulations based on real load demand and renewable generation data.

Approach for self‐healing resilient operation of active distribution network with microgrid

Self-healing capability as one of the most important features of smart distribution networks (SDN) causes an increase in the resiliency and flexibility of networks by responding fast and restoring service during an outage. A two-layer algorithm based on metaheuristics is proposed for the optimal operation of smart distribution network in self-healing mode considering microgrid (MG) and direct load control (DLC) programme capability in the presence of micro turbine (MT) and energy storage (ES) in the distribution network. In the first layer, a new approach based on the graph theory is proposed to find the optimal forming of MGs, while in the second layer the optimal energy management of MGs, including storages, is determined considering self-healing mode. To illustrate the advantage of the proposed method, the proposed algorithm is implemented on the modified IEEE 33 bus distribution network.