Microgrid Management Research Articles

Hardware Design for Implementation of Energy Management in a Solar-Interfaced DC Microgrid

The growing power requirement and the limited availability of fossil fuels makes it necessary to use renewable energy resources (RERs) as an alternative. The penetration of renewable sources in the existing power system becomes a big challenge for the safe and reliable operation of the system. Again, the erratic nature of these sources makes it impossible to supply constant power to the consumers. To overcome the intermittency of the environment and to provide constant reliable power to the loads, battery, fuel cell, and supercapacitor like storage devices are required in the system. Here, by considering the complementary characteristics of supercapacitors with high power density (500-5000W/L) and batteries with high energy density(50-80Wh/L), a combination of these two storage devices is used with renewable sources as a hybrid storage system. Hence, a proper power management scheme is necessary to control the power flow in the system and to balance the total power between generation and demand. In this paper, an energy management technique was proposed along with detailed modeling for the experimental setup of the DC microgrid (DCMG). A dSPACE 1103 controller was implemented in the hardware setup. The validation of the proposed energy management scheme and hardware design is done by using both simulation and experimental results.

Efficient voltage control of low voltage distribution networks using integrated optimized energy management of networked residential multi-energy microgrids

The optimal energy management system (EMS) of individual and networked residential microgrids and multi-energy microgrids (MEMGs) has received a great deal of attention. Several solutions have been studied to mitigate voltage limit violations due to the increasing penetration level of distributed generation resources (DGRs) and other issues. However, a research gap exists in developing a voltage control-oriented EMS for networked MEMGs (NMEMGs) to overcome the under/over voltage concerns. This paper aims to respond to this research gap by proposing a new voltage control-oriented EMS instead of conventional solutions, e.g., on-load tap changers (OLTCs) and limiting/curtailing output power of DGRs. The simultaneous management of the electrical and thermal supply/demand sides empowers the proposed method from the viewpoint of cost reduction and satisfaction of voltage constraints. Another contribution of this research is using the concept of sensitivity matrix information to calculate/control the voltage of NMEMGs, which is adequately fast and accurate. The voltage control of NMEMGs based on the flexibility of controllable residential thermal/electrical loads and appliances is one of the major advantages of this study. Moreover, the emission costs are considered in the proposed framework. The comparative test results illustrate that significant voltage constraint violations appear if the voltage limits are not considered. On the other hand, obtained results show that less than a 12% increase in the operation cost of some MGs/MEMGs has appeared due to additional constraints of the introduced voltage control-oriented EMS, while all voltage constraints are satisfied.

Energy Management of Hybrid DC Microgrid with Different Levels of DC Bus Voltage for Various Load Types

This article suggests a hybrid DC microgrid (HDCMG) with different levels of DC bus voltages to use for various types of loads. The available sources in the HDCMG are wind generating systems (WGSs), photovoltaic (PV) systems, battery banks, and the AC grid for emergencies. The various levels of the DC bus voltages are 760 V, 380 V, and 48 V for different application uses such as electric vehicles and home applications. In addition, the controller plays an important role in the proposed system to achieve the desired DC bus voltage levels and extract the maximum power point (MPP) from the WGS and PV systems. In order to check the power continuity for the critical loads and improve the overall system performance, a suggested energy management strategy (SEMS) is developed. The SEMS is based on the optimum generated power and the state-of-charge (SOC) of the battery banks. Further, the SEMS is developed as a way to prevent battery storage from overcharging and deep discharging. The mathematical relations of the proposed HDCMG and MPP tracking are described. The bidirectional 3-Φ inverter connects the 760 V bus voltage to the AC grid for regulating this DC bus by absorbing the excess power or supplying the required power during the shortage in the generation and the low SOC of the battery storage. Buck converters with controlled duty cycles rather than constant duty cycles are used to obtain 380 V and 48 V from 760 V to achieve better dynamic responses. The overall HDCMG is evaluated using the MATLAB/Simulink package under different working cases to verify the capability of the control system and the PEMS. The obtained results are discussed and show the good performance and the capability of the overall system under the different scenarios, including (i) a comparison between variable duty and constant duty; (ii) high/low generated power and the SOC of the battery in the acceptable region; (iii) high/low generated power and the SOC of the battery in the critical region; and (iv) high/low generated power and the SOC of the battery in the overcharging region.

Energy management for regional microgrids considering energy transmission of electric vehicles between microgrids

As the proliferation of electric vehicles (EVs) continues to accelerate, the inherent attributes of EVs warrant meticulous consideration in the realm of energy dispatch. In order to evaluate the ability of EVs as mobile energy storage, this paper presents an energy management framework for the microgrids' online dispatch, which accounts for the spatio-temporal energy transmission of EVs between microgrids. The energy management framework contains two iterative processes: optimizing charging price and guiding charging dispatch. To sufficiently capture the uncertainties of the renewable energy and load demand, chance-constrained optimization is utilized to determine the charging price by reasonable power allocation. To achieve a continuous and efficient control policy, a normalized advantage function-deep Q learning network (NAF-DQN) is developed for EV dispatch under V2G technology. The above two processes as a coupled optimization problem are solved alternately until convergence. Numerical cases considering energy transmission of EVs between microgrids are studied to demonstrate the superiority of the proposed dispatch framework. The simulation results indicate improved computational efficiency and higher-quality solution.

Energy Management in Microgrids with Renewable Energy Sources and Demand Response

This manuscript presents a unique method for stochastic management of renewable energy sources using Micro Grid (MG). This method is based on the demand reduction function to provide a demand side reserve to supply electricity to consumers. To reduce peak demand and energy costs, a typical MG for distributed generation (DG) is used to schedule 24-hour power. The optimal generation scheduling problem is solved as both single-objective and multi-objective optimization problems. Differential Evolution and Multi-Objective Differential Evolution are used to minimize operating costs and environmental emissions, which consider equality and inequality constraints to find the best way to allocate power among the many units connected to MG. Power allocation using DE is shown to be more cost effective and environmentally friendly compared to Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) based technique.

Real time validation of a control system for microgrids with distributed generation and storage resources

Faced with a scenario in which the increase in renewable energy generated near consumer centers can cause problems for the operation of the electrical network, the present work proposes a real-time simulation model for automation and control systems of electrical distribution networks with microgrids, distributed generation, and storage resources. The proposed model consists of a hardware-in-the-loop control with the aid of a simulation tool in conjunction with a Real-Time Digital Simulator (RTDS) and considers the dynamic behavior of switched elements and inverters. A communication platform using TCP/IP protocol between RTDS (power system) and MatLab/Simulink (optimization algorithms) allows the operation of the network in grid-connected and islanded mode, guaranteeing the computational time for experimental implementation. For the first mode, an algorithm is proposed to solve an optimal dispatch energy storage system problem. Second mode, an algorithm is proposed to solve a load shedding problem. The objective is to operate the microgrids optimally and evaluate the performance of a storage system based on real data from the state of Paraná, in Brazil. Results show that the optimization algorithms are experimentally applicable, obtaining reasonable computational time to find optimal solutions and an assertive decision-making to meet the objectives. Thus, the proposed framework is a potential tool to validate algorithms for active management of microgrids in real-time simulation.

An effective quantum artificial rabbits optimizer for energy management in microgrid considering demand response

Solving the energy management (EM) problem in microgrids with the incorporation of demand response programs helps in achieving technical and economic advantages and enhancing the load curve characteristics. The EM problem, with its large number of constraints, is considered as a nonlinear optimization problem. Artificial rabbits optimization has an exceptional performance, however there is no single algorithm can solve all engineering problem. So, this paper proposes a modified version of artificial rabbits optimization algorithm, called QARO, by quantum mechanics based on Monte Carlo method to determine the optimal scheduling for MG resources effectively. The main objective is minimization of the daily operating cost with the maximization of MG operator (MGO) benefit. The operating cost includes the conventional diesel generator operating cost and the cost of power transactions with the grid. The performance of the proposed algorithm is assessed using different standard benchmark test functions. A ranking order for the test function based on the average value and Tied rank technique, Wilcoxon's rank test based on median value, and Anova Kruskal–Wallis test showed that QARO achieved best results on the most functions and outperforms all other compared technique. The obtained results of the proposed QARO are compared with those obtained by employing well-known and newly-developed algorithms. Moreover, the proposed QARO is used to solve two case studies of day-ahead EM problem in MG, then the obtained results are also compared with other well-known optimization techniques, the results demonstrate the effectiveness of QARO in reducing the operating cost and maximization the MGO benefit.

Twin-delayed deep deterministic policy gradient algorithm for the energy management of microgrids

The microgrid market is growing significantly due to several drivers, such as the need to lower greenhouse gas emissions by integrating higher shares of distributed renewable energy sources, falling costs of microgrid components, the need for more reliable power supply infrastructures, and new off-grid solutions to foster electricity access in developing economies. Coordinated management of the microgrid components is crucial for their effectiveness, and this can be very challenging when hosting solar or wind generation. This paper studies the energy management problem of a microgrid based on reinforcement learning algorithms. The advantage of using these algorithms against other optimization and machine learning techniques is that they do not need past experiences to learn a strategy. The learning is based on trial and error experiences, which facilitates its easy implementation to other microgrids while demonstrating their facility to be applied in real cases. In particular, this paper proposes an implementation for an Energy Management System (EMS) in microgrids using the Twin-Delayed Deep Deterministic Policy Gradient (TD3) algorithm. Moreover, it compares the proposed algorithm with the Deep Q-Network (DQN). This comparison evaluates the improvement over exploiting the continuous nature of the decision variables against a discretization of the same since the DQN cannot make actions over a continuous space.

Inverter Control Design for the optimal management of electrical Microgrids

Nowadays, the promotion of renewable energy sources is a necessity. Technically, the increase of the renewable penetration in the power system (PS) is a challenge. One solution for this problem is the use of self-controlled microgrids (smartgrid, SG). In this work, the design of an algorithm to optimal manage the SG energy through the power inverter control in the SG is presented. In general, a SG consists of renewable energy sources, electrical loads and storage systems. In an isolated SG (no connected to the power system), a DC/AC (direct current / alternating current) converter (power inverter, PInv) is necessary to provide an alternating voltage to supply the loads. If the SG is connected to the PS, the PInv is used to inject the active power from the renewable energy sources.

A Risk-Averse Adaptive Stochastic Optimization Method for Transactive Energy Management of a Multi-Energy Microgrid

This paper proposes a new energy management method for a multi-energy microgrid (MEMG) which supplies both electrical and thermal energies. Based on the transactive energy (TE) concept, the problem is formulated as a Stackelberg game-theoretic bi-level optimization model. The MEMG operator optimizes the energy scheduling and pricing strategies at the upper level, and the industrial, commercial and residential agents optimize their energy trading strategies at the lower level. The equivalent single-level mixed-integer linear program (MILP) reformulation is then derived for computational tractability. To coordinate the strategies made in the day-ahead and intra-day energy markets, an adaptive stochastic optimization (SO) approach is adopted, by which a day-ahead stochastic MILP and an intra-day deterministic model are formed. Furthermore, the conditional value-at-risk (CVaR) measure is incorporated in the day-ahead stage for risk aversion of the MEMG operator towards uncertainties. To solve the models, an adaptive Progressive Hedging (PH) algorithm is developed to decompose the day-ahead stochastic MILP into multiple scenario-based subproblems which can be solved in parallel, and an outer approximation (OA) algorithm is adopted in the intra-day stage to linearize the bilinear objective function. Finally, the simulation results verify the effectiveness of the proposed energy management method and the efficiency of the solution algorithms.

Editorial on the Special Issue Entitled “Regulatory Frameworks Addressed to Promote Renewable Energy Sources and Microgrids. Regulatory Constraints and Implications on Conception, Design and Energy Management of Microgrids”

As renewable energy sources and microgrids have developed, the need to use a transversal approach for dealing with issues on the design and energy management of these assets is clear. Although renewable energy sources and microgrids are used within the electricity sector in a significant number of countries, the technical and scientific literature often dismisses the effects that in-force regulatory frameworks have on this type of assets. This Special Issue aims to enlighten readers on the relationship between the electricity sector’s regulatory frameworks and microgrids, providing clear evidence on the need to consider regulatory constraints in their design and energy management.

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.

A pricing game strategy with virtual prosumer guidance in community grid

AbstractThe peer‐to‐peer (P2P) energy trading model is considered to be one of the appropriate models for managing local energy trading in community grids. But free pricing mechanisms can lead to energy wastage and long response times. In order to facilitate the local consumption of renewable energy within the community, this paper proposes a pricing game strategy with virtual prosumer (VP) guidance on a P2P energy market model in community grid. To prevent market disruption caused by free pricing strategies in the P2P market and ensure the overall social benefit of the community, a VP controlled by the community microgrid manager is introduced to regulate the energy trading of the entire community. A novel and efficient prosumer trading model is proposed where the VP changes its pricing strategy to guide other prosumers to achieve Nash equilibrium and improve the internal consumption rate of new energy in the community. The proposed strategy's rationality and effectiveness are verified through case studies.

Flexible Smart Energy-Management Systems Using an Online Tendering Process Framework for Microgrids

Currently, modern power grids are evolving into complex cyber-physical systems integrated with distributed energy resources that can be controlled and monitored by computer-based algorithms. Given the increasing prevalence of artificial intelligence algorithms, it is essential to explore the possibility of energy management in microgrids by implementing control methodologies with advanced processing centers. This study proposes a novel smart multi-agent-based framework under a tendering process framework with a bottom-up approach to control and manage the flow of energy into a grid-connected microgrid (MG). The tendering organization in this structure as an upstream agent allocates demand among generators, creates a balance between supply and demand, and provides optimal energy cost for the MG. To optimize the electricity cost and decrease the use of grid power, the first-price sealed-bid (FPSB) algorithm is implemented over the tendering process. The proposed approach from one side optimally allocates energy among generators, and, from the other side, guarantees the system from blackouts. Theoretical analysis and results demonstrate that the proposed technique is easy to implement and provides a robust and stable control for MGs, which can guarantee energy management as well as flexible and online control. Furthermore, results show the proposed framework besides the real-time allocation of power among providers to optimize the injected power from the grid so that the total injected power by the grid is 146.92 kWh and the injected power to the grid is 214.34 kWh.

Predictive control of interlinked water-energy microgrids

Although water and energy systems are interdependent, they are still designed individually due to their complexity. This paper proposes a multiple input multiple output (MIMO) model predictive control (MPC) for demand management of an integrated water distribution system (WDS) supplied by an islanded microgrid (MG) system, offering an additional opportunity for energy-saving at the WDS. This strategy benefits the island and remote communities, which have limited access to the centralized electric grid. The existing literature either used an open-loop approach for energy management of water-energy microgrids or ignored the interconnected nature and MIMO nature of these systems. Compared to that, the proposed closed-loop energy management system is developed to address the detailed dynamics of assets, the inherent interconnections between water and energy networks, and to provide resilience as well as energy saving and demand management capabilities. The integrated MPC algorithm is designed in discrete time, utilizing a state-space model and sequential quadratic programming (SQP) as the solution method. Considering the intensive use of electricity by the pumps in WDS, their energy consumption is accommodated by an islanded microgrid system that incorporates a diesel generator, renewable energy sources (wind and solar), and a battery bank that serves as an energy source and a storage device. The proposed approach is investigated using base water demand from a real-world community, Telford, in Pennsylvania. Several case studies, such as variations in renewable energy contributions and increments of the control actions, are carried out to demonstrate the feasibility and reliability of the integrated MPC to provide efficient water and energy demand management concurrently.

Novel Renewable Energy Source in Standalone Microgrid Application with Island Load Management

Renewable energy sources, which use natural sources to generate energy, are currently replacing conventional energy sources to fulfil the world’s expanding energy demands. However, photovoltaic and wind energy, which are among these types of energy sources, are not steady and not available for 24 hours in a day. As an alternative, it is possible to extract and use hot water from the earth all the time. Therefore, this study proposes a thermoelectric generator that uses hot water from the hot spring area to generate free electricity as a new renewable energy source in a microgrid application. In this system, the microgrid is standalone without a battery storage system because the thermoelectric generator is in operation for 24 hours in a day (day and night). In this way, the cost of the battery is eliminated by removing it from the system. In this paper, a novel generation method is proposed in microgrid applications as a renewable energy source. In addition, load management system was implemented to control three types of residence loads. The main advantage of this study is to generate electricity from renewable sources like photovoltaic and thermoelectric to demand without much wasting energy by controlled loads according to the proposed load management algorithm. In this study, the microgrid and energy management control is modeled with MATLAB/Simulink. The results showed that the essential load is always active through thermos electric generator besides the absence of battery storage system.

Physics-model-free heat-electricity energy management of multiple microgrids based on surrogate model-enabled multi-agent deep reinforcement learning

Reliability and cost-effectiveness in the operation of the multiple microgrid (MMG) system depend on the skillful management of its energy resources. Traditional energy management approaches are physics-model-based, which rely on the precise system parameters (e.g., line parameters) of the electricity and heat network. It is difficult to establish the precise system parameters in practice because they depend on a variety of factors. In this context, this paper proposes a physics-model-free control framework for the energy management of MMGs with heat-electricity energy, consisting of proposed surrogate model and multi-agent deep reinforcement learning (MADRL) approach. An important step is to use historical data to train a surrogate model in supervised manner that can imitate the realistic power and thermal flow calculations. Meanwhile, the energy management problem is reformulated as a Markov game. It is solved by the proposed MADRL-based approach by modeling each MG controller as an agent with a specific objective. The historical trajectories representation, parameter space technology, and deep dense architecture in reinforcement learning are introduced in MADRL to overcome the negative impact brought by the time series data from the input state on the decision-making process and construct an efficient exploration mechanism to overcome inefficient optimization of the MMG system in a multi-agent setting. During training period of MADRL, the trained surrogate models integrate into the environment of the MADRL, which can develop optimal energy management strategy based on the continuous interaction with the surrogate models. The proposed surrogate model enabled MADRL approach can reduce the reliance on precise physical systems and prevent having an impact on the real system while being trained involving trial and error process. Simulation results demonstrate the effectiveness of the proposed control framework.

Multi-agent deep reinforcement learning-based optimal energy management for grid-connected multiple energy carrier microgrids

Multi-agent deep reinforcement learning (MA-DRL) method provides a groundbreaking approach to tackling computational problems in power systems, particularly for distributed energy resources that have been widely adopted to advance energy sustainability. This paper presents a novel optimal energy management based on proposed MA-DRL method. This method employs deep neural network to learn strategy based on stacked-denoising auto-encoders and multi-agent deep deterministic policy gradient learning capability. The MA-DRL method is adopted to find the optimal strategy of the optimal energy management problem under the Markov decision process framework. This method aims to coordinate multiple energies and achieve optimal operation over a variety of hourly dispatches while taking into account the distinct properties of electric and thermal energies. The primary challenge of the planning and operation of multiple energy carrier microgrids (MECMs) is determining the optimal interaction between renewable energy resources, energy storage systems, power-to-thermal conversion systems, and upstream power grid in order to improve overall energy utilization efficiency. The presented robust method can adaptively derive the optimal operation for MECMs through centralized learning and decentralized implementation. The optimization problem is employed in this study to concurrently reduce the total emissions and the operating costs while considering engineering design constraints. Finally, to demonstrate the efficiency of the proposed method, it is verified on an integrated modified IEEE 33-bus and 8-node gas systems.

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.

Modelling and simulation for energy management of a hybrid microgrid with droop controller

<span lang="EN-US">The most efficient and connected alternative for increasing the use of local renewable energy sources is a hybrid microgrid, these systems face additional challenges due to the integration of power electronics, energy storage technologies and traditional power plants. The hybrid <a name="_Hlk120713679"></a>alternating current-direct current (AC-DC) microgrid that is the subject of this research uses a primary-droop control system to regulate state variables and auxiliary services, thus, it is composed of batteries, solar panels and a miniature wind turbine (PDC) and controls how each energy source in a microgrid contributes to the final product. To achieve the given objectives, this paper will create appropriate models for each part of the microgrid design and define, among them, the energy storage batteries and power electronic converters required for each level of each of these systems. Finally, the dynamic nature of the system will be critically evaluated and characterized, to distribute the load and reduce imbalances, modify the primary drop of the resulting microgrid using MATLAB simulation.</span>