Microgrid Energy Research Articles

Optimizing design and dispatch of a resilient renewable energy microgrid for a South African hospital

Lack of access to reliable energy is a major concern for countries in sub-Saharan Africa. The national grids are unable to consistently satisfy demand. Therefore, users turn to distributed generation systems in the form of back-up generators. However, such systems are usually designed based on a rule of thumb. We employ a mixed-integer linear programming model that considers several options such as renewable energy, combined heat and power, and storage technologies, in addition to those on-site, to provide optimal design and dispatch decisions that minimize total cost. We apply this model to a case study for a hospital in South Africa, considering its need for reliable electricity in light of multiple outages that might occur over the course of a year, as well as its high heating and cooling loads. Our results show that optimal design and dispatch decisions for the distributed generation system address reliability challenges, regardless of the time at which they occur. And, these solutions yield millions of dollars in savings, suggesting that technologies such as the absorption chiller may be overlooked in typical designs; its integration can reduce demand charges even in the absence of combined heat and power. We show that total cost is most sensitive to changes in site electrical demand, followed by capital cost, fuel cost, photovoltaic production, and monthly demand charges; changes in fuel cost primarily affect system sizes of combined heat and power and the absorption chiller, while photovoltaic system size is more sensitive to the changes in capital and fuel costs, photovoltaic resource availability, and hourly electrical demand. Finally, an outage simulator demonstrates the ability of our optimized system to sustain with no interruptions in power five-hour outages with probability 1.0 and ten-hour outages with probability 0.65, significant improvements over 0.5 and 0.0, respectively, under a business-as-usual case.

Short-term load forecasting for microgrid energy management system using hybrid SPM-LSTM

Load forecasting in power microgrids and load management systems is still a challenge and needs an accurate method. Although in recent years, short-term load forecasting is done by statistical or learning algorithms. There are still two unsolvable challenges in the conventional data-driven-based prediction methods. The first challenge is that extracting the correlation between metrological and load data still cannot be taken full advantage of, and the other is that extracting patterns independently of the fixed pattern length is not supported. To address these challenges, a fixed SPM-LSTM approach is proposed. SPM for discrete data and LSTM for continuous data. The proposed model uses past consumption, temperature, humidity, wind speed, and weather data. A sequential pattern mining algorithm is used to extract sequential patterns that are independent of the fixed pattern length of correlated data between load and metrological data and can predict only the range of future load. Then, an LSTM network is used for exact load forecasting. It was tested using real data from the consumption and generation data retrieved from the ENTSOE project and its performance was compared with other forecasting methods. Results have shown that the proposed approach with the correlation squared (R2) of 0.951 in the best situation outperformed other methods like LSTM, LSTM-ANN, and CNN-GA. Also, the proposed method reduced training time by one-fifth against others.

Energy Performance Analysis of Photovoltaic Integrated with Microgrid Data Analysis Using Deep Learning Feature Selection and Classification Techniques

Microgrids are an essential element of smart grids, which contain distributed renewable energy sources (RESs), energy storage devices, and load control strategies. Models built based on machine learning (ML) and deep learning (DL) offer hope for anticipating consumer demands and energy production from RESs. This study suggests an innovative approach for energy analysis based on the feature extraction and classification of microgrid photovoltaic cell data using deep learning algorithms. The energy optimization of a microgrid was carried out using a photovoltaic energy system with distributed power generation. The data analysis has been carried out for feature analysis and classification using a Gaussian radial Boltzmann with Markov encoder model. Based on microgrid energy optimization and data analysis, an experimental analysis of power analysis, energy efficiency, quality of service (QoS), accuracy, precision, and recall has been conducted. The proposed technique attained power analysis of 88%, energy efficiency of 95%, QoS of 77%, accuracy of 93%, precision of 85%, and recall of 77%.

Optimal sizing of battery energy storage in solar microgrid considering peak load shaving

The power system faces a lot of challenges due to incorporation of a large number of renewable energy sources. Here, Battery Energy Storage System (BESS) can be used effectively to deal with the grid’s rising peak demands without the necessity of upgrading the grid capacity. An energy management strategy is considered based on peak load shaving combined with transformer power limit and BESS State of Charge (SOC). Cooperation Search Algorithm (CSA) is utilised to determine the number of batteries in BESS and Photovoltaic (PV) panels with the goal of maximising economic benefits. The optimisation model of solar microgrid energy storage is established and solved for three scenarios of the grid only (Base Case), BESS without PV (Case-A) and BESS with PV (Case-B). From the result, it is verified that in Case-B, the customer will pay 31% and 34% less compared to the Base Case and Case-A, respectively, towards energy costs in a day.

Stochastic optimal scheduling strategy for a campus-isolated microgrid energy management system considering dependencies

Isolated microgrids have been widely used on campuses, becoming an important part of their power-supply infrastructure. In this study, a stochastic optimal scheduling strategy that considered dependencies was proposed for the energy management system (EMS) of an isolated microgrid on campus, which consists of three main components: an accurate forecasting model on both the demand and supply sides, an excellent core scheduling strategy, and an effective uncertainty analysis. First, the forecasting model involved data correction, feature selection, core forecasting, and error analyses to obtain consistently good forecasting results. A multilayer hybrid forecasting model was developed, including a stacked multilevel denoising autoencoder, mutual information, bidirectional long short-term memory network (LSTM), and LSTM, corresponding to the above four model elements. Based on the forecasting results, improved episodic deep reinforcement learning was used to build a stochastic optimal scheduling strategy for the EMS, which could realize rapid propagation of reward values and achieve stable updating of episodic memory, effectively improving sample efficiency. The objective of optimizing the scheduling was to obtain the lowest operational cost. Finally, the uncertainties in the forecasting process were analyzed using the Latin hypercube sampling method, and a regular vine copula was used to determine the dependence of the uncertainty analysis between buildings. To the best of our knowledge, this is the first study to consider dependence in the uncertainty analysis of a campus microgrid. The performance of the proposed method was evaluated by comparing it with other existing models, aiming to obtain more efficient and stable scheduling results.

Survey on AI and Machine Learning Techniques for Microgrid Energy Management Systems

In the era of an energy revolution, grid decentralization has emerged as a viable solution to meet the increasing global energy demand by incorporating renewables at the distributed level. Microgrids are considered a driving component for accelerating grid decentralization. To optimally utilize the available resources and address potential challenges, there is a need to have an intelligent and reliable energy management system (EMS) for the microgrid. The artificial intelligence field has the potential to address the problems in EMS and can provide resilient, efficient, reliable, and scalable solutions. This paper presents an overview of existing conventional and AI-based techniques for energy management systems in microgrids. We analyze EMS methods for centralized, decentralized, and distributed microgrids separately. Then, we summarize machine learning techniques such as ANNs, federated learning, LSTMs, RNNs, and reinforcement learning for EMS objectives such as economic dispatch, optimal power flow, and scheduling. With the incorporation of AI, microgrids can achieve greater performance efficiency and more reliability for managing a large number of energy resources. However, challenges such as data privacy, security, scalability, explainability, etc., need to be addressed. To conclude, the authors state the possible future research directions to explore AI-based EMS's potential in real-world applications.

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.

Administration strategy of energy management in smart grid: system view and optimization path

Power generation and transmission infrastructure is vulnerable to the interaction of various Distributed Generations (DG), which leads to the imbalance of power system operation, frequent voltage drops or spikes, and even power outages. This phenomenon not only wastes energy, but also affects grid security. The main reason is a delayed feedback of circuit failure and load changes, and the optimization of energy management system and path is an effective way to solve the above problems. In this paper, a method of multi-objective optimization based on ANFIS algorithm is proposed which can help to improve the demand response, energy storage and management of smart power grid, reduce the volatility of DGs, reducing electricity costs and improving energy efficiency. Firstly, based on the ANFIS algorithm, the distributed power generation control mode, inverter control, real-time electricity price calculation method, energy transfer and storage scheme are improved, and the optimization path of the energy management system is defined. Secondly, the advantages of ANFIS algorithm in response speed and running stability are verified by comparing with other algorithms. Finally, a distributed energy microgrid is constructed for simulation verification. The results show that :(1) ANFIS optimization algorithm has good adaptability in smart grid, and has advantages in large amount of data processing and information transmission; (2) The verification model based on ANFIS has strong elasticity and efficient response speed. The research results will help solve various problems in the smart grid, including establishing a clear energy management system path, maintaining the stable operation of the power system, providing users with more reasonable power plans and the lowest cost of electricity.

ASSESSING FEASIBILITY STUDIES ON SMART MICROGRID SYSTEMS: A GLOBAL REVIEW AND METHODOLOGICAL COMPARISON FOR IMPLEMENTING MICROGRIDS IN MALAYSIA

This paper serves as a comprehensive review of past feasibility studies conducted worldwide on smart microgrid systems. The primary focus of microgrids lies in the generation of electricity using micro sources, such as micro-hydro, photovoltaic, or biomass gasifiers. These generated power is then distributed through low voltage networks, catering to a relatively small number of consumers. Malaysia, owing to its geographical location, possesses abundant renewable resources, making it highly suitable for the implementation of microgrids. The adoption of microgrids in Malaysia holds significant potential, offering numerous benefits to the nation and its residents. Prior to initiating a feasibility study within Malaysia, it is crucial to conduct a preliminary study to gain insights into the methodologies employed in previous studies. This review paper concentrates on comparing the methodologies utilized in various studies, including the feasibility study of microgrid installation in an educational institution with grid uncertainty, the feasibility study for 100% renewable energy microgrids in Switzerland, the feasibility study of a microgrid village employing renewable energy sources, the feasibility study for a solar PV microgrid in Malawi, and the feasibility study of microgrid applications in Langkawi and Socotra Islands. By thoroughly examining these studies, this review paper aims to identify the most optimal methodology for conducting feasibility studies on microgrids.

RETRACTED ARTICLE: Optimizing residential DC microgrid energy management system using artificial intelligence

RETRACTED ARTICLE: Optimizing residential DC microgrid energy management system using artificial intelligence

A robust optimization model for microgrid considering hybrid renewable energy sources under uncertainties.

Hybrid renewable energy sources and microgrids will determine future electricity generation and supply. Therefore, evaluating the uncertain intermittent output power is essential to building long-term sustainable and reliable microgrid operations to fulfill the growing energy demands. To address this, we proposed a robust mixed-integer linear programming model for the microgrid to minimize the day-ahead cost. To validate the proposed model piecewise linear curve is to deal with uncertainties of wind turbine, photovoltaic, and electrical load. The proposed solution is demonstrated through a case study compared under a robust worst-case scenario, deterministic model, and max-min robust optimization that aim to find optimal robustness. So, a piecewise linear curve is considered to obtain uncertain parameters in order to deal with uncertainties and predict the day-ahead cost. This study illustrates how the Uncertainty Budget Set selection used to integrate renewable energy sources into a microgrid, which manages the energy system. Therefore, the model complexity was slightly modified by adjusting the Uncertainty Budget Set to obtain the optimal decision and control the load demand and uncertainty of renewable energy sources. The comparative results demonstrate that the proposed robust optimization can achieve high solutions under microgrid's availability and is intended to confirm that the proposed method is more cost-effective than alternative optimization techniques. Additionally, the effectiveness and advantage of the proposed methodology in the IEEE 33-node system are validated in this case study by comparing it to the existing optimization. The comparison results show that the proposed robust optimization methods illustrate the model's efficiency, concluding remarks, and managerial insights of the research.

Energy and Economic Analysis of Renewable Energy-Based Isolated Microgrids with AGM and Lithium Battery Energy Storage: Case Study Bigene, Guinea-Bissau

By the year 2020, 90% of the population with access to electricity worldwide was surpassed. However, the reality is very different for many countries, especially for those on the African continent that had more than 572 million people without electricity service at the end of 2019. This work studies the implementation of an isolated microgrid activated with photovoltaic energy and energy storage in batteries under the case study of the community of Bigene, located in the African country of Guinea-Bissau. This type of project is a potential solution to the problem of access to energy, but as the cost of the energy storage system is typically very high, this work technically and economically addresses the effect of using absorbed glass material (AGM) and lithium batteries. A simulator was developed using TRNSYS software to analyze the operation of the microgrid under a defined annual demand profile for different types of users, and economic analysis was conducted considering a project lifetime of 25 years. The results showed no significant differences in the solar fraction of both types of batteries when the photovoltaic power was less than 600 kW, regardless of the capacity of the storage bank. The analysis of auxiliary power requirements showed that lithium technology leads to a lower consumption from 800 kW of PV capacity, and utilizing less than this capacity did not have a significant difference with AGM batteries. In this microgrid with a photovoltaic capacity of less than 700 kW and an energy storage of less than 2580 kWh, the type of storage technology, AGM or lithium, did not represent a considerable difference in the levelized cost of energy, indicating that AGM technology could be selected considering its low initial investment cost compared to lithium batteries.

Optimal Allocation of Energy Storage Capacity in Microgrids Considering the Uncertainty of Renewable Energy Generation

The high dimensionality and uncertainty of renewable energy generation restrict the ability of the microgrid to consume renewable energy. Therefore, it is necessary to fully consider the renewable energy generation of each day and time period in a long dispatching period during the deployment of energy storage in the microgrid. To this end, a typical multi-day scenario set is used as the simulation operation scenario, and an optimal allocation method of microgrid energy storage capacity considering the uncertainty of renewable energy generation is designed. Firstly, the historical scenarios are clustered into K types of daily state types using the K-means algorithm, and the corresponding probability distribution is obtained. Secondly, the Latin hypercube sampling method is used to obtain the state type of each day in a multi-day scenario set. Then, the daily scenario generation method based on conditional generative adversarial networks is used to generate a multi-day scenario set, combining the day state type as a condition, and then the typical scenario set is obtained using scenario reduction. Furthermore, a double-layer optimization allocation model for the energy storage capacity of microgrids is constructed, in which the upper layer optimizes the energy storage allocation capacity and the lower layer optimizes the operation plans of microgrids in each typical scenario. Finally, the proposed model is solved using the PSO algorithm nested with the CPLEX solver. In the microgrid example, the proposed method reduces the expected annual total cost by 19.66% compared with the stochastic optimal allocation method that assumes the scenic power obeys a specific distribution, proving that it can better cope with the uncertainty of renewable energy generation. At the same time, the expected annual total cost is reduced by 6.99% compared with the optimal allocation method that generates typical daily scenarios based on generative adversarial networks, which proves that it can better cope with the high dimensionality of renewable energy generation.

Optimal Scheduling Strategy for Multi-Energy Microgrid Considering Integrated Demand Response

Research on energy storage plants has gained significant interest due to the coupled dispatch of new energy generation, energy storage plants, and demand-side response. While virtual power plant research is prevalent, there is comparatively less focus on integrated energy virtual plant station research. This study aims to contribute to the integrated energy virtual plant station research by exploring the relationship between the integrated energy electro-thermal coupling capacity, various forms of electro-thermal integrated energy response, and electro-thermal integrated energy storage. Analyzing the attributes of an integrated energy microgrid, including energy storage characteristics, time-of-use tariffs, and electric and thermal loads, is crucial. A grid-connected microgrid with cogeneration systems, electric boilers, fuel cells, and energy storage systems is used as an illustrative example. The dispatching method prioritizes multiple complementary energy sources while considering the integrated energy demand response. The study presents different models for the electricity demand and thermal energy demand response and introduces the design of a wholesale power trader involved in building energy storage facilities and participating in the demand response. To verify the feasibility and rationality of the integrated energy demand response scenario, three different schemes are compared, and an economic analysis is conducted.

Optimal design and sizing of renewable energies in microgrids based on financial considerations a case study of Biskra, Algeria

Integration of renewable energy sources in microgrids is a challenging process, where a wealth of metrics should be optimized together to achieve an optimal design. This paper presents a comprehensive approach for selecting the best microgrid structure including a versatile renewable energy source (RES), the proposed microgrid systems are considered using investment costs evaluation and total power generation needs. Through the analysis of the economic feasibility and guiding decision-making on microgrid projects, three system configurations have been examined at the Algerian site of Biskra using the HOMER software. In this work, photovoltaics, photovoltaics/wind, and photovoltaics/wind/diesel projects were explored in respect to the specificities of the targeted region. The Net Present Cost (NPC) was found to be 11.7 M$,13.3 M$, and 9.45 M$ respectively, likewise, Levelized Cost of Energy (LCOE) were 0.19 $/kWh, 0.728 $/kWh, and 0.188 $/kWh respectively. Where a comparative analysis was carried out as a sensitivity analysis to find out the effect of different economic and technical conditions on costs and parameters. Cash flows were also forecasted, and financial criteria such as the Internal Rate of Return (IRR) (13%, 15%), Net Present Value (NPV) (1 M$ to 10 M$) for three projects, and Discounted Payback (DPb) (6 to 9 years). In addition, Monte Carlo simulation is used to assess the risks associated with the project's net present value, this is done to maintain the robustness of project decision-making outcomes. The findings emphasise the importance of financial analysis in the design and sizing of RES microgrid.

Green supply chain management: a renewable energy planning and dynamic inventory operations for perishable products

The emergence of renewable energy microgrids (REM)-supported logistics parks reflects the urgent social requirement for sustainability facing energy crisis and climate change. Focusing on the emergency REM capacity planning and dynamic inventory operations problem for a REM-supported cold warehouse, this paper constructs a novel two-stage stochastic optimization method to comprehensively minimise long-term investment and short-term emergency operation costs of the system, while improving the resilience of energy-constrained supply chain management of perishable products. In particular, we formulate the second stage emergency operation problem as a Markov decision process that considers the interaction between energy and operational decisions constrained by intermittent of renewable energy, the randomness of the occurrence time, and the balance between power supply and temperature changes of the cold warehouse. We propose a novel two-phase PSO-based MPC algorithm to solve the entire problem. We also examine the effectiveness of our algorithm via an empirical study based on the data in East China. Nine typical scenarios clustered from the real data suggest different optimal capacity planning and operational policies. The empirical results show that it can reduce an overall 9% of operating costs on average compared to other heuristic policies. Moreover, installing REM can improve the resilience of power systems by 23% (risk impact area reduction ratio) and improve the resilience of inventory systems by 49%.

Weighting Factor Design Techniques for Predictive Control of Power Electronics and Motor Drives

The rapid growth of energy demand requires progressive energy generation. This, together with the demand for higher efficiency and flexibility, has promoted the application of power electronics in energy systems. During the past decade, model predictive control (MPC) of power electronics has witnessed significant advancements in both dynamic performance and optimal control of the multi-objective terms. Several of these terms can have equal control priorities, resulting in a symmetrical cost function; however, most objectives have different priorities and require weighting factors to resolve the asymmetry in the cost function. Currently, researchers continue to encounter challenges in the optimal design of weighting factors. Moreover, the relative performance of different techniques that either utilize or avoid the weighting factor are uncertain. Therefore, this study focuses on weighting factor design techniques in the literature as applied to wind/solar energy conversion, microgrids, grid-connected converters, and other high-performance converter-based systems. These are grouped under the heuristic, offline tuning, sequential, and online optimization methods. This study demonstrates that optimal online tuning of weighting factors and sequential MPC methods can both offer improved robustness against parameter uncertainties. In addition, the advantages and limitations of different techniques are highlighted.

Reliability-constrained Bi-level collaborative planning of distribution network and micro-energy grid

In order to make full use of the potential reliability value of micro-energy grid (MEG) to defer further grid investment and reduce the energy supply cost, this paper proposes a bi-level collaborative planning model for distribution network and micro-energy grid considering network reliability. With consideration of reliability difference, price signals are used to guide the optimal balance between supply and demand. The upper-level aims at maximizing the profit of the distribution network while optimizing the pricing and expansion planning strategies of the distribution network. And the lower-level aims to minimize the cost of micro-energy grid as well as optimizing the planning and operation strategies of micro-energy grid. A 14-node distribution network system is utilized to conduct the simulation, the results show that the proposed method can improve the economic benefits of distribution network and micro-energy grid and reduce the overall energy supply cost on the premise of meeting users' demands. In addition, with the increase of users' reliability requirements, the effect of reducing the overall energy supply cost is more significant.© 2017 Elsevier Inc. All rights reserved.

Simulation Model and Performance Analysis for Distributed Compressed Air Energy Storage under Off-design Conditions

The dual carbon target in China, which is set to improve the low carbon and economy of regional microgrid (villages in northwest China for example) energy consumption, is proposed to use compressed air energy storage to improve the utilization rate of distributed new energy and reduce the cost of power purchase to the upper power grid. Therefore, the system dynamic characteristics simulation and performance analysis under wide working conditions are very important for improving the utilization rate of new energy. Firstly, the operation characteristics of the compression subsystem and turbine subsystem under wide working conditions are analyzed. Secondly, the mathematical models of the compression subsystem, turbine subsystem, throttle valve, and air storage chamber in the distributed compressed air energy storage system are established. Finally, the dynamic characteristics of energy storage and energy release under different working conditions of the system are studied through simulation. The simulation results show that under the condition of the high mass flow rate, the pressure of the air storage chamber can be increased by 8.29 MPa, and the temperature can be increased by 82 °C. The research results provide a theoretical basis for the system design of distributed compressed air energy storage.

Modeling and optimization of hybrid microgrid energy system: a case study of University of Abuja, Nigeria

This research work modelled and optimized the hybrid microgrid energy system for electricity generation at the University of Abuja, Nigeria, using PV, wind, diesel, and battery renewable energy resources. The model and optimization of the system are performed through HOMER software. The estimated university average annual power consumption is 2355 kWh/day, and the optimal load demand is 313.40 kWp. The PV/wind/diesel/ converter/battery hybrid system has the lowest cost of energy (COE) of 0.1616 $/kWh, operating cost of $50,592, and net present cost (NPC) of $1,795,026 but diesel/wind/converter/battery hybrid system has highest COE of 0.4242 $/kWh and NPC of $4,710,983. The optimal total electricity generated is 1,272,778 kWh/yr while electricity generated by PV contribute the highest energy of 1,030,485 kWh/yr (81%), whereas diesel generator and wind produced energy of 93,927 kWh/yr (7.38%) and 148,366 kWh/yr (11.7%) respectively. The wind/diesel/converter/battery hybrid system produced carbon dioxide (CO2) of 557,749 kg/yr. The most environmentally friendly is the wind/PV/battery and PV/battery hybrid system without pollutants emissions, but the diesel/wind/battery hybrid system has the highest rate of pollutants emissions. The result shows that PV’s electrical power is extremely high from February to June, which causes a high rate of irradiance within the specified period.