Microgrid Power Research Articles

Editorial: Evolutionary multi-objective optimization algorithms in microgrid power dispatching

Editorial: Evolutionary multi-objective optimization algorithms in microgrid power dispatching

Island microgrid power control system based on adaptive virtual impedance

When the microgrid is in the islanding operation mode, affected by the line impedance difference between the distributed power sources (DGs), the traditional droop control strategy will lead to the fact that the reactive power of the system cannot be reasonably distributed according to the droop gain, which makes the power grid prone to failure. To solve this problem, an improved sag control strategy based on adaptive virtual impedance is proposed in this paper. The central controller calculates the given power according to the capacity of each inverter and the total load capacity of the line, and then sends it to each inverter, and the inverter determines the value range of the virtual impedance according to the given reactive power. A voltage compensation link is added to the control strategy to compensate for the voltage drop difference caused by the line impedance difference, thereby ensuring the stability of the output power and realizing the power control of the microgrid. The MATLAB/Simulink simulation platform was built to establish the microgrid simulation model in island mode. The simulation results show that when the inverters with the same capacity are connected in parallel, the output active power is the most stable, and the reactive power maintains an equalized state; When inverters of different capacities are connected in parallel, the output active power takes the shortest time to stabilize, and the reactive power basically matches the rated capacity ratio. Therefore, it is fully demonstrated that this method has a good control effect on the power control and operation of the islanded microgrid.

Optimal-sustainable multi-energy management of microgrid systems considering integration of renewable energy resources: A multi-layer four-objective optimization

This paper presents a techno economic-environmental energy scheduling framework for the multi-energy microgrid system. The suggested model couples the electrical, heating, cooling, and water sections to enhance the flexibility and reliability of the microgrid system. A multi-layer energy scheduling framework has been performed to simultaneously optimize operating cost, carbon emission, underground water extraction, and independency of the multi-energy system. The first layer of the proposed model emphases on the optimal cost-effective operation management of the multi-energy microgrid system. The second layer considers the environmental problems and presents a multi-objective framework to minimize underground water extraction and emission pollution simultaneously. The main advantages of the second layer are that it prevents land subsidence, deterioration of water quality, reduction of water in streams and lakes, drying up of wells, and emission pollution. The third layer tries to enhance the independence of the multi-energy microgrid system from the upstream network to simultaneously optimize the power losses, voltage profile, efficiency, and power quality. The multi-layer model is verified on a typical case study, and the optimization results demonstrate that the independence of the multi-energy microgrid systems and power losses have been improved by 22.81 % and 17.59 %, respectively.

Distributed energy sharing algorithm for Micro Grid energy system based on cloud computing

AbstractThe reduction of adverse environmental effects and the socioeconomic advantages of renewable energy systems promote greater integration of distributed energy systems into the traditional electrical networks. A new type of sharing economy is emerging with the sharing of energy resources to reduce transaction costs by using platform services in the cloud. Given the obstacles posed by the legacy system and various forms of renewable energy integration, Distributed Energy and Micro Grids (DE‐MG) are an efficient means of raising the quality of energy services. Rules for microgrid scalability, maintaining a budget, and security can make this difficult. Consumers are better at receiving the best renewable energy allotment price using a cloud‐based Peer‐to‐Peer (P2P) network. The main objective is to lower the daily energy cost of microgrid power in commercial buildings. In the proposed work, cloud‐based P2P for peer‐Multi Agent System (p‐MAS) optimization techniques are used to reduce system peak and integrated Demand Response (DR) with Energy Management System (EMS) in a commercial MG. To fill knowledge gaps about how various power market architectures and individual decision‐making processes impact local interactions and market outcomes, cloud‐based P2P for Modelling Leveraging Agents (MLA) is used for bill calculation. A performance measure is finally created for cost evaluation and reliability to measure the social benefits of cloud‐based P2P models for exchanging energy. For various price environments and resource types, a comparison between the proposed cloud‐based P2P model with an existing P2P model for exchanging energy is provided. The primary use of a distributed P2P model for exchanging power in a microgrid is to reduce electricity costs and increase grid environment reliability.

Microgrids Multiobjective Design Optimization for Critical Loads

Since microgrids with renewable generation and energy storage can achieve high reliability, they present an attractive solution for powering critical loads. Microgrids should be carefully planned and optimized to meet the power requirements of critical loads and justify their economic viability. Conventional microgrid design approaches consider a fixed power architecture, focusing mainly on improving the financial aspects of the design by sizing its energy sources. This paper introduces a new Vectorial Microgrid Optimization (VMO) design method for critical loads. The proposed VMO improves the microgrid design by 1) incorporating the selection of the microgrid power conversion architecture and the size of the energy sources into a unified design strategy, 2) implementing multi-objective optimization to find the desired balance between the microgrid power supply availability, net present cost, and power efficiency. Through the use of graph theory, the proposed VMO automates the architecture selection process. The benefits of the proposed VMO method are highlighted in a critical load case-study to find the optimal microgrid design. The obtained microgrid design is compared with the results of existing microgrid planning tools and the standard industry approach for powering critical loads.

A Decentralized Power and Bus Voltage Regulation Approach for DC Microgrids

In this paper a decentralized power regulation approach is presented for converters operating in DC microgrids. The method enables classical droop control to perform power regulation tasks by employing auto-adaptive virtual resistances for the controlled devices. Moreover, the auto-adaptive nature of the droop characteristic is employed to constraint, through a decentralized approach, the microgrid DC bus voltage within an admissible range of values, despite unpredictable changes in the operating conditions of the microgrid (i.e., sudden variations in load and/or in renewable production). The proposed approach is formalized as a multi-objective optimization problem whose solution is entrusted to a method based on dynamic system theory and Lyapunov theory. Simulations are carried out on test DC microgrids, in order to prove the approach effectiveness.

Enhancing the Conventional Controllers for Load Frequency Control of Isolated Microgrids Using Proposed Multi-Objective Formulation via Artificial Rabbits Optimization Algorithm

Isolated microgrids (IMGs) power remote areas. However, IMG may lower the frequency stability and increase frequency excursions with low system inertia. Load frequency management ensures system stability. Thus, the paper proposes a novel multi-objective tuning strategy to improve IMG’s load frequency control (LFC) and take the microgrid controller’s control signals into account. Diesel engine generator, fuel cell, battery energy storage system, and renewable energy sources (RESs) like photovoltaic and wind systems make up the IMG. Conventional controllers such as proportional-integral (PI) and proportional integral derivative (PID) are classically tuned based on the standard error criteria as a traditional single-objective tuning approach. Due to the low inertia of the system and the stochastic nature of RES, they cannot act as required under different operating scenarios. Therefore, the PI and PID controllers are tuned using the proposed multi-objective-based tuning approach to reduce the frequency deviations. In addition, anti-windup is applied to the enhanced classic controllers to keep them distant from the nonlinear zone and beyond the source’s physical constraints. The proposed tuning process also considers the maximum practical generation rates for different sources. The recent Artificial Rabbits Optimization (ARO) algorithm is applied to simultaneously adjust the controller parameters for several controlled sources in IMG. Extensive simulations in MATLAB and Simulink confirm the effectiveness of the proposed approach to keep the system stable even when facing high levels of disturbances. In addition, accomplishing sensitivity analysis, severe ±25% changes to the system’s parameters guarantee that the proposed tuning strategy keeps the system stable.

Probabilistic Optimal Power Flow for Balanced Islanded Microgrids

This paper presents a Probabilistic Optimal Power Flow (POPF) for losses minimization in balanced islanded microgrids considering load uncertainties following a normal distribution. The peculiarities of the islanded operation, like the absence of infinite bus and frequency variation in the face of load and loss variations, are adequately considered. The constraints deal with the power balancing, angular reference, droop control mode of generators (P-f e Q-V), and the probability of satisfying the limits of microgrid frequency, nodal voltages, and power generation. As the main contribution, the Differential Evolution method and the Unscented Transformation solve the POPF, reducing the computational time and providing accurate results compared with the Monte Carlo Simulation. The proposed approach is evaluated using a 33-bus islanded microgrid.

Johansen model for photovoltaic a very short term prediction to electrical power grids in the Island of Mauritius

<abstract> <p>Sudden variability in solar photovoltaic (PV) power output to electrical grid can not only cause grid instability but can also affect power and frequency quality. Therefore, to study the balance of electrical grid or micro-grid power generated by PV systems in an upstream direction, predicting models can help. The power output conversion is directly proportional to the solar irradiance. Unlike time horizons predictions, many technics of irradiance forecasting have been proposed, long, medium and short term forecasting. For the Island of Mauritius in the Indian Ocean, and regards to key policy decisions, the government has outlined its intention to promote the PV technologies through the local electricity supplier but oversee the technical requirements of PV power output predicts for 1 hour to 15-minutes ahead. So, this paper is illustrating results of the Johansen vector error correction model (VECM) cointegration approach, from the author original and previous studies, but for a very short term prediction of 15-minutes to PV power output in Mauritius. The novelty of this study, is the long run equilibrium relationship of the Johansen model, that was initially determined in previous research works and from dataset in Reunion Island, is then applied to the PV plant in the Island of Mauritius. The proposed prediction model is trained for an hourly and 15-minutes period from year 2019 to year 2022 for a random month and a random day. The experimental results show that the performance metric R<sup>2</sup> values are more than 93% signifying that Johansen model is positively and strongly correlated to onsite measurements. This proposed model is a powerful predicting tool and more accuracy should be attained when associated to a machine learning method that can learn from datasets.</p> </abstract>

PBI Based Multi-Objective Optimization via Deep Reinforcement Elite Learning Strategy for Micro-Grid Dispatch With Frequency Dynamics

Micro-grid dispatch is facing a great challenge since the system security and economic cost are often time-varying with different requirements. In this paper, a novel multiple time-scale dispatch model is firstly constructed with considering load voltage and frequency dynamics of islanding awareness, which is limited with rate-of-change of frequency (RoCoF) of micro-grid. Based on this model, micro-grid power dispatch is converted into a multi-objective optimization problem (MOP) with taking economic cost, voltage deviation and frequency stability into consideration. To address this problem, a penalty-based boundary intersection (PBI) based multi-objective optimization approach is developed with an elite learning technique. In the above approach, a developed deep deterministic policy gradient (DDPG) is proposed to learn evolutionary parameters with grid based alternating weight vectors, which can lead to better convergence ability and diversity distribution. Simulation results show that the proposed optimization strategy can properly achieve minimum economic cost with simultaneously satisfying voltage and frequency stability, which provides a viable and promising way for tackling with micro-grid dispatch problem.

Energy Management System and Enhancement of Power Quality with Grid Integrated Micro-grid using Adaptive Fuzzy Logic Controller

Microgrids (MGs) are a flexible way to incorporate renewable energy sources into the grid. Micro grid (MG)offers a different way to generate electricity at the supply voltage level through installation of small distributed energy sources. Through their interconnection, the power electronics components contribute to MG's adaptability and dependability. Power quality issues are caused by grid’s many non-linear, unbalanced loads. Intelligent controllers must be implemented to boost Micro-grid power quality, battery characteristics, MPPT for solar, wind power. Adaptive controllers are favored in this study. By controlling charging, discharging of battery units in response to generation-demand mismatches, battery status, integrated micro-grids that make use of adaptive fuzzy logic controllers and fuzzy logic controllers can reduce frequency fluctuations. In this article, MPPT is achieved by the application of Petrurb & Observe (P&O), Incremental Conductance (IC) technique. The proposed model's dynamic behaviour is analyzed over operational conditions. During grid isolation, measurements of solar irradiance, temperature, wind speed are taken from connected systems, calculations of actual, reactive power are made. The results are obtained by MATLAB/ SIMULINK .

Deep-Q-network-based energy management of multi-resources in limited power micro-grid

Deep-Q-network-based energy management of multi-resources in limited power micro-grid

Deep-Q-Network Based Energy Management of Multi Resources in Limited Power Micro-grid

Deep-Q-Network Based Energy Management of Multi Resources in Limited Power Micro-grid

Modeling and Optimization of Isolated Combined Heat and Power Microgrid for Managing Universiti Teknologi PETRONAS Energy

Modeling and Optimization of Isolated Combined Heat and Power Microgrid for Managing Universiti Teknologi PETRONAS Energy

저탄소 커뮤니티 설계를 위한 건축물 에너지공유 시나리오 개발

The government announced microgrid power plants and power and thermal networks as a means of carbon neutrality in the building sector. This is related to the use of distributed resources, the expansion of zero-energy buildings, and the realization of regional carbon neutrality. Since the community consists of buildings for various purposes, it is necessary to understand the supply and demand system according to the use of the building to build a low-carbon energy sharing community. In this study, the effect of energy sharing was analyzed by designing a virtual community using the existing community, virtual buildings, and photovoltaic power systems. The energy sharing scenarios between buildings in the community were developed in six ways, including the base scenario, and the application results of the scenario were analyzed as annual electricity rates.

Seamless plug-in plug-out enabled fully distributed peer-to-peer control of prosumer-based islanded AC microgrid

Recent innovations in microgrids consumers converted into Prosumers (local generator + local load). This paper proposes a fully distributed peer-to-peer (P2P) control strategy for the prosumer-based islanded alternating current (AC) microgrid (MG). Prosumers would be independent to operate as producers or consumers. The proposed scheme uses droop control with each distributed generator (DG) to balance local generation and demand, while gossip communication-based distributed P2P control is employed with each Prosumer to restore voltage and frequency deviations across the MG. The prosumers are synchronised concerning their nearest point of common coupling (PCC) instead of a single PCC to avoid the effective line impedance mismatch among the DGs that causes circulating current flow among the DGs and results in poor reactive power-sharing. A prosumer-based islanded AC MG consisting of three differently rated Prosumers and a common load has been modelled in the MATLAB/Simulink environment and simulated with the Opal-RT 4510 real-time simulator to validate the effectiveness of the proposed study. The captured results and the frequency response analysis shows accurate power-sharing by the prosumer-DGs, seamless strategic or individual plug-in plug-out, and consumer operation of prosumers maintaining the MG's power quality and stability.

Development of economic processes utilizing solar energy

The concern of the electric utility companies to prioritize tight control on system performance is addressed in the paper as it immensely helps planning maintenance expenditure economically with customers’ expectations fully met. . The paper aims at making a comparative study of performance of the micro-grid power system with that of the interconnected micro-grid and national grid. A micro-grid that utilizes solar energy in sparse locations in hilly terrain for ensuring reliable electric supply to industrial houses engaged in product manufacturing processes has been considered. These manufacturing processes as a part of small and medium industries introduce goods and services necessary for securing economic growth and development in the country side where grid power supply is subject to frequent weather disturbances, and hence, very costly from the perspective of sustenance as well as maintenance. The solar energy trend has given scope for self-reliance and better livelihood of people who live away from cities but the products what they manufacture may be sent for consumption in the nearby cities. Thus, more and more dependence on the solar energy leads to nation’s economic growth policy. Moreover, it adds to uninterrupted and quality electric supply using clean energy in place of fossil fuels-based generation of electricity to protect environment; allows reducing health hazards and mitigating greenhouse gas emissions. Using reliability techniques, solar energy plant performance is quantitatively judged and compared with respect to the performance of the national grid supplying power supply to the remotely located population. The improved energy security, easy access and operational responses of the solar plant as a substitute to the grid power supply help develop economic growth of a nation.

Energy management and harmonic compensation of micro‐grids via multi‐agent systems based on decentralized control architecture

AbstractHere, a new energy management strategy based on a multi‐agent structure is presented by introducing hybrid control of AC micro‐grid (MG), where current harmonics compensation is also considered. This energy management strategy is based on Mealy machine in a multi‐agent system (MAS), which is implemented by generating coordination signals and sending them to the renewable energy sources (RES). In RES, maximum power point tracking (MPPT) or reverse of Hill‐climbing (RHC) is used based on these received coordination signals to limit the exchange power between the MG and the grid. Also, the multi‐agent structure of shunt active power filters (SAPFs) and passive filters have been studied in power MGs. The comparison of MG current harmonic compensation by these filters by fast Fourier transform (FFT) algorithm has been investigated in the proposed network. The simulation results show that the proposed energy management system (EMS) with proper management of MG different agents has been able to have a suitable performance for hybrid distributed generation (DG) systems. Also, the SAPF with better and more appropriate harmonic compensation than the SC‐RC‐LCL passive filter can eliminate the harmonic pollution of MG and be used with high performance in hybrid DG systems.

Sharing sequence components of reactive power in a three-phase four-wire islanded microgrid

Under any load disturbances, the microgrid must maintain its voltage and frequency within the standard norms. In an isolated microgrid, under-loading and overloading conditions may emerge if the sequence components of powers are not appropriately shared by the power electronics interfaced distributed generation systems (DGSs). Hence, this paper tends to offer a droop-based control strategy for a three-level neutral point clamped (NPC) inverters-based islanded microgrid that enables supply and maintains power-sharing effectively. The proposed method constitutes enhanced voltage and current controllers, sequence components-based virtual impedance (VI) loop, and frequency restoration loop. With the proposed droop-based control, an equal and proportional power-sharing is achieved. Furthermore, the presented VI loop allows the connected DGs with uneven line impedances to share the positive and negative sequence components of the reactive power under unbalanced loads. Besides, the upgraded voltage controllers in the stationary reference frame ensure that the DG's terminal voltages remain balanced, even when the loads at point of common coupling (PCC) demand uneven currents, while the frequency restoration loop maintains the system frequency very close to the nominal value (i.e., 50 Hz). Various scenarios such as equal and proportional power sharing and multiple numbers of DGs are considered in the study to validate the efficacy of the proposed control scheme. The simulation results are carried out in MATLAB/Simulink environment to demonstrate the efficiency of the suggested control scheme.

Development of a Supervisory System Using Open-Source for a Power Micro-Grid Composed of a Photovoltaic (PV) Plant Connected to a Battery Energy Storage System and Loads

The importance of renewable energies and energy storage system forming a micro-grid and integrating it to the electrical grid is widely spread. A supervisory system plays a crucial role in controlling, managing, and planning the micro-grid. This paper demonstrates the development of a new custom supervisory system based on Internet of Things (IoT), creating an information sharing environment. The proposed supervisory system is based on open-source tools for a micro-grid, composed of a photovoltaic power plant and a storage system, employing smart devices and making non-smart devices compatible with IoT systems. The new supervisory improves the available system by incorporating new features and devices and increasing the data polling rate when necessary. A comparison between the current supervisory system and the proposed one is performed, showing that the new system is more flexible, easily modified, cost-effective, and more fault-resilient.