Community Microgrid Research Articles

Research on Multi-Scenario Variable Parameter Energy Management Strategy of Rural Community Microgrid

Many rural communities in western China use renewable energy-based clean energy supply methods, and the community microgrid system of “photovoltaic + energy storage + electric heating” has been widely used. However, the energy management effect of such a typical rural community microgrid system is not very satisfactory. Aiming at the problem of the comprehensive economic operation of the microgrid system in rural communities, a method for optimizing the operation strategy parameters of typical operating scenarios is proposed, and critical parameters of the real-time energy management strategy of the microgrid system are optimized. Through the evaluation of the annual time-scale simulation results, it is proved that the proposed strategy can improve the comprehensive economic benefits of the operation of the rural community microgrid system (comprehensive economic benefits include electricity bills and energy storage system (ESS) life loss). Relying on the actual community microgrid demonstration project system in western China, a community-level microgrid energy management monitoring system is built. The control strategy proposed in this paper is applied to the demonstration system for experimental verification. The operational data of the demonstration system shows that the proposed method is feasible and effective.

Modeling and frequency control of community micro-grids under stochastic solar and wind sources

The Community Micro-Grid (CMG) is a coordinated local grid area served by one or more distribution substations supported by local renewable and other distributed energy resources (DER). In an islanded mode, the CMG stability reduces due to low inertia of the equivalent system and stochastic nature of renewable energy sources (RESs). To study the frequency stability of the islanded CMG system, this paper explores the mathematical modeling aspects of different sources in the CMG and the robust control design for the community microgrid model. The robust design using a fixed structure H∞ synthesis method has been presented to compensate for the effect of modeling uncertainty. The uncertainty in the CMG model results in parametric perturbation which degrades its performance and stability. This paper explores the existing mathematical models of CMG’s and different control techniques applied for frequency regulation. Robust controller design for CMG is proposed that can handle the stochastic input disturbances like a sudden change in power coming from solar and wind sources and also the model uncertainty that results in the parametric perturbations. The complete model is simulated using MATLAB Simulink platform and results of frequency deviation obtained by robust and PID controller are compared. The controller hardware in loop (C-HIL) validation of the robust control method for CMG has been done using a real-time controller board (DS1104). This CHIL system also implements the CMG plant-controller model and analyzes the frequency deviation against load and renewable power fluctuations. The oscillations in frequency deviation are analyzed for sudden rise or fall in the renewable power coming from the solar and wind sources. A reduction of 30% is achieved in frequency overshoot and settling time using a robust controller than the PID controller. The controller developed and implemented using H∞ synthesis has robust stability and performance for the perturbed CMG model and also shows a better time response, unlike the PID controller.

Short-Term Load Forecasting Using Modified Sequence-to-Sequence Deep Learning Framework

The future of smart grids promises unprecedented flexibility in energy management. Therefore, it is crucial to accurately predict the energy load demand of individual power grid stations and overall levels. In this paper, a Sequence to Sequence learning method based on long short-term memory neural network (Seq2Seq) forecast model is proposed to predict the load of the community microgrid. In particular, this paper studies the effectiveness of Seq2Seq in community microgrid load forecasting, avoiding overfitting through weight attenuation regularization, and reducing the impact of interference and noise on training data. The proposed method is implemented on the base load data set of multi-user. The results of Seq2Seq were compared with the results of two well-known methods, one is traditional LSTM and the other is Support Vector Machine (SVM) on the same datasets. Experimental results show that compared with SVM and traditional LSTM, Seq2Seq has better learning effect. Finally, we calculate the performance index. When using load data to train and test the model, it was noted that compared with traditional LSTM and SVM, the root mean square error (RMSE) of Seq2Seq was decreased by 44.07% and 64.06%, respectively. Our research offers a decent reference for the application of deep learning in the field of energy management.

An energy management model for isolated microgrid community considering operation flexibility

The isolated microgrids adjacent to each other form an isolated microgrid community, which can realize optimal resource allocation among microgrids with different source-load characteristics through energy sharing, thereby improving the economical, reliability and renewable energy utilization of regional power system operation. Aiming at the energy management problem of the isolated microgrid community, based on the multi-agent system, a hierarchical energy management architecture of the isolated microgrid community was established. On this basis, the flexibility index is introduced to evaluate the impact of renewable energy output uncertainty on system operation. A multi-objective optimization energy management strategy as well as a community layer energy allocation strategy is constructed. The linearized model is solved by the tolerant lexicographic method. Finally, an isolated microgrid community consisting of four microgrids is taken as an example to verify the effectiveness of the proposed energy management strategy.

Transactive energy coordination mechanism for community microgrids supplying multi‐dwelling residential apartments

A transactive energy coordination mechanism is proposed in this study where community microgrids are supplying power to multi-dwelling residential apartments. The proposed transactive energy coordination mechanism coordinates the energy sharing among apartments based on the energy profile of the community microgrid where the excess energy is traded with non-contributing apartments. The proposed coordination mechanism is embedded within an energy management controller which uses the energy profile and determines the valuation of energy. A choice factor along with the bound on electricity prices is also incorporated to calculate the bidding price and a double-sided auction mechanism is considered for the bidding purpose. The utility maximisation approach is used to clear the market. Different scenarios based on the flexibility in the pricing strategy are considered to evaluate the performance of the proposed transactive energy coordination mechanism. The potential economic benefits of the proposed scheme are also analysed which clearly demonstrate that it is beneficial for all participants.

An integrated blockchain-based energy management platform with bilateral trading for microgrid communities

In this paper, an integrated blockchain-based energy management platform is proposed that optimizes energy flows in a microgrid whilst implementing a bilateral trading mechanism. Physical constraints in the microgrid are respected by formulating an Optimal Power Flow (OPF) problem, which is combined with a bilateral trading mechanism in a single optimization problem. The Alternating Direction Method of Multipliers (ADMM) is used to decompose the problem to enable distributed optimization and a smart contract is used as a virtual aggregator. This eliminates the need for a third-party coordinating entity. The smart contract fulfills several functions, including distribution of data to all participants and executing part of the ADMM algorithm. The model is run using actual data from a prosumer community in Amsterdam and several scenarios of the model are tested to evaluate the impact of combining physical constraints and trading on social welfare of the community and scheduling of energy flows. The scenario variants are trade-only, where only a trading mechanism is implemented, grid-only where only OPF optimization is implemented and a combined scenario where both are implemented. Results are compared with a baseline scenario. Simulation results show that import costs of the whole community are reduced by 34.9% as compared to a baseline scenario, and total energy import quantities are reduced by 15%. Total social welfare is found to be highest without a trading mechanism, however this platform is only viable when all costs are equally shared between all households. Furthermore, peak imports are reduced by over 50% in scenarios including grid constraints.

Unpacking the complexity of community microgrids: A review of institutions’ roles for development of microgrids

Community microgrids implemented in existing electricity grids can meet both development targets set out in the Paris agreement: 1. mitigate greenhouse gas emissions through increased implementation of renewable energy sources, and 2. to adapt to climate related disturbances and risk of catastrophes. Community microgrids are, however, complex to implement and institutional change is needed to reach their full potential. The purpose of this article is to review existing literature and analyze institutional developments influencing the growth of community microgrids. The literature describes a concentration of microgrid activities in specific regions: USA, EU, Asia and Australia. Varying reasons for implementing community microgrids were found in the different regions but similar institutional developments occurred, albeit with differing emphasis due to contextual specificities. Formal directions do however influence informal institutions even though their aims differ. Power utilities stand out as a critical actor and both formal and informal institutions put pressure on utilities to update their traditional business models. This article illustrates how informal and formal institutions play a significant role in the growth of community microgrids in existing electricity grids and provide interesting examples which can be utilized by policymakers. Microgrid development is still in a formative phase and further institutional change in the form of updated regulations is needed.

Heuristic Enhanced Evolutionary Algorithm for Community Microgrid Scheduling

Scheduling of community microgrids (CMGs) is an important and challenging optimization problem. Generally, the optimization is performed to schedule resources of CMGs at minimum cost. In recent years, a number of algorithms have been proposed to solve such problems. However, the performance of these algorithms is far from ideal due to the presence of different complex equality and inequality constraints in CMGs. Furthermore, most of the current works ignore energy storage (ES) degradation costs in the optimization model, which has a significant impact on the life of ES. This paper develops both single and bi-objective optimization models by considering the life of ES along with the operating cost for scheduling a CMG. An efficient heuristic-enhanced Differential Evolution (DE) approach is proposed to solve these models; by exploiting the structure of equality constraints, the proposed heuristic is able to generate feasible solutions quickly. The significance of the proposed heuristic is that it can generate a high-quality solution with a considerably lower computational effort. Numerical simulations were performed to evaluate the performance of the proposed method, and obtained results were compared with the state-of-the-art algorithm. The simulation results corroborate the efficacy of the proposed method.

Blockchain Enabled Distributed Demand Side Management in Community Energy System With Smart Homes

Existing work in energy demand side management focuses on the interaction between the utility grid and consumers. However, the previous technique is not focused on energy trading in local community of a renewable energy generation, distributed demand side management and not suitable for real-time environment. This paper presents a distributed demand side management system among multiple homes in community microgrid, with the integration of the internet of things smart meter and in the presence of renewable energy sources. The proposed energy consumption game is formulated for minimizing the cost of electricity in the individual home and the total cost of energy consumption in the whole community. The smart home users are playing game by optimizing their own daily energy consumption of appliances. The multiple participants include the self renewable generation of users, shared community microgrid and optional utility company. Each participant applies its best strategy to minimize energy consumption cost and users can maintain their own privacy of energy consumption. Moreover, the proposed scheme is distributed on blockchain, which provides a trusted communication medium between the participants. It enforces the autonomous monitoring of smart appliances and the billing of electricity consumption via smart contracts. Solidity smart contract is deployed to facilitate the execution of transactions without the involvement of third party in the smart community. Comparison of the results show that the proposed approach minimizes the total cost of energy consumption as well as each user's energy consumption cost.

A Multi-Timescale Two-Stage Robust Grid-Friendly Dispatch Model for Microgrid Operation

Uncertainty in renewable energy generation and load consumption is a great challenge for microgrid operation, especially in islanded mode as the microgrid may be small in size and has limited flexible resources. In this paper, a multi-timescale, two-stage robust unit commitment and economic dispatch model is proposed to optimize the microgrid operation. The first stage is a combination of day-ahead hourly and real-time sub-hourly model, which means the day-ahead dispatch result must also satisfy the real-time condition at the same time. The second stage is to verify the feasibility of the day-ahead dispatch result in worst-case condition considering high-level uncertainty in renewable energy dispatch and load consumptions. In the proposed model, battery energy storage system (BESS) and solar PV units are integrated as a combined solar-storage system. The BESS plays an essential role to balance the variable output of solar PV units, which keeps the combined solar-storage system output unchanged on an hourly basis. In this way, it largely neutralizes the impact of solar uncertainty and makes the microgrid operation grid friendly. Furthermore, in order to enhance the flexibility and resilience of the microgrid, both BESS and thermal units provide regulating reserve to manage solar and load uncertainty. The model has been tested in a controlled hardware in loop (CHIL) environment for the Bronzeville Community Microgrid system in Chicago. The simulation results show that the proposed model works effectively in managing the uncertainty in solar PV and load and can provide a flexible dispatch in both grid-connected and islanded modes.

Robotic Expert System for Energy Management in Distributed Grid Ecosystem

A robotic expert system (RES) for energy management (EM) in community-based micro-grids is developed using a fuzzy computational scheme. Within the micro-grid multi-dimensional space, embedded algorithms for residential homes, sectors and central controller units are introduced to perform EM in a collaborative manner. Demand response and load shedding are carried out within the community micro-grid to ascertain the behavioral responses based on changes in power demand levels. Various tests are carried out with an observable low error margin. It was observed that the system reduced the total power demand on the micro-grid by 20% of the total distributed power. Micro-grid RES, neuro-fuzzy control (NFC), and support vector regression (SVR) evaluations are compared considering the home units at 40kW of the generated capacity. The results gave a 35.79%, 31.58% and 32.63% energy demand, respectively. Consequently, RES provides a grid look-ahead prediction, annotated-self healing, and stability restoration.

Short Circuit Fault Discrimination Using SiC JFET Based Self-Powered Solid State Circuit Breakers in a Residential DC Community Microgrid

This article identifies and validates the use of ultrafast silicon carbide (SiC) junction field effect transistor (JFET)-based self-powered solid-state circuit breakers (SSCBs) as the enabling protective device for a 340 Vdc residential dc community microgrid. These SSCBs will be incorporated into a radial distribution system in order to enhance fault discrimination through autonomous operation. Because of the nature and characteristics of short-circuit fault inception in dc microgrids, the time–current trip characteristics of protective devices must be several orders of magnitude faster than conventional circuit breakers. The proposed SSCBs detect short-circuit faults by sensing the sudden voltage rise between its two power terminals and draw power from the fault condition itself to turn off SiC JFETs and then, coordinate with no-load contacts that can isolate the fault. Depending upon the location of the SSCBs in the microgrid, either unidirectional or bidirectional implementations are incorporated. Cascaded SSCBs are tuned using a simple resistor change to enable fault discrimination between upstream high-current feeds and downstream lower current branches. Operation of one of the SSCBs and three in cascaded arrangements are validated both in simulation and with a hardware test platform. Thermal impact on the SSCB is discussed as well. The target application is a residential dc microgrid that will be installed as part of a revitalization effort of an inner city Milwaukee neighborhood.

A distributed Peer-to-Peer energy transaction method for diversified prosumers in Urban Community Microgrid System

As massive integration of Distributed Energy Resources (DERs), the role of end-users in the Urban Community Microgrid System (UCMS) has transformed from traditional consumers into prosumers with capabilities of both energy production and consumption. The exchange of energy between autonomous microgrid prosumers can be achieved with the introduction of Peer-to-Peer (P2P) energy transaction, promoting the efficient allocation of energy in the UCMS. However, the existing centralized P2P energy transaction approaches require microgrid transaction brokers to obtain prosumers’ private data, including energy resource configuration, operation status, and energy production/consumption schedule. With the enhancement of prosumers’ awareness of privacy protection, it will be increasingly more difficult for the brokers to obtain such private data in practical application scenarios, resulting in obstacles on the implementation of such centralized approach. Thus, a novel distributed P2P energy transaction method based on the double auction market is proposed in this paper. Prosumers first generate the information of energy supply and demand autonomously utilizing distributed energy management model, then set the price targeting profit maximization, and finally initiate P2P energy transaction mutually in the double auction energy market. Compared with the existing centralized approaches, the method proposed in this paper can achieve the coordination and complementarity of energy in the UCMS, promoting economic benefit, energy self-sufficiency, and renewable energy self-consumption without sacrificing privacy preservation and robustness.

Design and environmental sustainability assessment of small-scale off-grid energy systems for remote rural communities

Small-scale off-grid renewable energy systems are being increasingly used for rural electrification, commonly as stand-alone home systems or community micro-grids. With the variety of technologies and configurations available, it is not clear which options are sustainable for remote communities. This study investigates the life cycle environmental sustainability of both home and community installations, designed as part of this work, which utilise diesel, solar, and wind resources coupled with battery storage. A total of 21 system configurations (six home systems and 15 micro-grids) have been designed and optimised for a prototypical rural community in the Philippines, considering both stand-alone and hybrid systems. Life cycle assessment (LCA) considering 18 potential impact categories has been carried out to compare the environmental impacts associated with electricity production of each option. At the household level, hybrid solar photovoltaics (PV)-wind systems with storage have 17–40% lower impacts than the equivalent stand-alone installations per kWh generated. Batteries are a major environmental hotspot, causing up to 88% of the life cycle impacts of a home energy system. Among the community micro-grid options, the PV-wind-lead acid battery hybrid system has the lowest impacts in many categories, including climate change, ozone depletion, and acidification. Comparing equivalent architectures for single-household and community-scale installations, PV systems are environmentally more sustainable if installed individually in households, while larger turbines in community micro-grids are environmentally better for wind utilisation. The results suggest that a household-scale PV system integrated within a micro-grid with community-scale wind turbines and Li-ion batteries is environmentally the most sustainable configuration.

Energy sharing through shared storage in net zero energy communities

Growth in the adoption of distributed energy resources is shaping a new energy ecosystem posing a perceptible threat to the grid by relying on it as a virtually inexpensive storage mechanism. This growth is compounded by new policy objectives that require pursuing net zero energy (NZE) goals for new buildings. One emerging framework that attempts to remediate this problem is energy sharing in a community microgrid. In this framework, through complementary demand profiles and shared energy storage, buildings use energy resources more efficiently with the objective of reducing grid interactions. In this paper, we create a year-long discrete-time simulation model of 40 residential and non-residential buildings to measure the reduction in grid interactions through energy sharing and shared storage for the case of a NZE community. Our results show that, when sharing is enabled, a 9.5% reduction in grid interactions can be obtained with buildings that have energy storage. Additionally, a month-by-month exploration revealed that annual patterns in generation drastically impact the benefits from sharing energy. The reduction in grid utilization ranged from 20% during periods of high energy surplus (i.e. summer) to 5% during low energy generation (i.e. winter).

Distributed charging management of multi‐class electric vehicles with different charging priorities

This study proposes distributed energy management approach for charging multi-class electric vehicles (EVs) in community microgrids. The energy management problem is implemented in real-time and represented by a non-cooperative Stackelberg game for the power distribution inside the microgrid. In this game, a battery energy storage system is chosen as a leader and the EVs are designated as followers. The charging power distribution among EVs is tackled in the two cases of ‘plenty of power’ and ‘lack of power’. The challenging case of ‘lack of power’ occurs when the total charging power is insufficient to meet the need of each EV, such as when weather conditions are unfavourable. A priority factor is included in the EV utility functions to address charging priorities of different classes of EVs in practical scenarios. A consensus-based distributed algorithm is developed later to iteratively reach the Nash equilibrium, i.e. final charging power distribution, among EVs with different charging priorities. Both simulation and experimental results show that the charging power is properly distributed when the predefined charging priorities are followed, particularly in the case of a ‘lack of power’.

Optimal Sizing and Analysis of a Hybrid Energy System for a Community Microgrid in Nigeria

Optimal Sizing and Analysis of a Hybrid Energy System for a Community Microgrid in Nigeria

Energy scheduling of community microgrid with battery cost using particle swarm optimisation

The integration of renewable energy sources together with an energy storage system into a distribution network has become essential not only to maintain continuous electricity supply but also to minimise electricity costs. The operational costs of this paradigm depend highly upon the optimal use of battery energy. This paper proposes day-ahead scheduling of the battery energy while considering its degradation costs due to charging-discharging cycles. The degradation costs with respect to the depth of charge are modelled and added to the objective function to determine the actual operational costs of the system. A framework to solve the function is developed in which particle swarm optimisation, the Rainflow algorithm and scenario techniques are integrated. Uncertainties of parameters, modelled by scenario generation and reduced by scenario reduction techniques, are discussed. Simulation results demonstrate that the proposed method can reduce the operational costs by around 40% compared to the baseline method. They also reveal that uncertainty in power generation and power demand has no influence on the energy schedule of the battery, but variation in electricity prices has an impact on the outcome. Several pragmatic tests verify the effectiveness of the proposed method.

Peer-to-Peer Energy Trading in a Prosumer-Based Community Microgrid: A Game-Theoretic Model

This paper proposes a novel game-theoretic model for peer-to-peer (P2P) energy trading among the prosumers in a community. The buyers can adjust the energy consumption behavior based on the price and quantity of the energy offered by the sellers. There exist two separate competitions during the trading process: 1) price competition among the sellers; and 2) seller selection competition among the buyers. The price competition among the sellers is modeled as a noncooperative game. The evolutionary game theory is used to model the dynamics of the buyers for selecting sellers. Moreover, an M-leader and N-follower Stackelberg game approach is used to model the interaction between buyers and sellers. Two iterative algorithms are proposed for the implementation of the games such that an equilibrium state exists in each of the games. The proposed method is applied to a small community microgrid with photo-voltaic and energy storage systems. Simulation results show the convergence of the algorithms and the effectiveness of the proposed model to handle P2P energy trading. The results also show that P2P energy trading provides significant financial and technical benefits to the community, and it is emerging as an alternative to cost-intensive energy storage systems.

MicroFEWs: A Food-Energy-Water Systems Approach to Renewable Energy Decisions in Islanded Microgrid Communities in Rural Alaska.

In recent years, there has been increased recognition of the importance of a nexus approach to optimize food, energy, and water (FEW) security at regional and global scales. Remote communities in the Arctic and Subarctic regions in Alaska provide unique examples of closed and isolated systems, wherein the FEW nexus not only needs to be examined to lend resilience to these vulnerable communities but that could also serve as small-scale test beds for a wider and systematic understanding of the FEW nexus. In this short communication, looking at the FEW nexus in Cordova, Alaska, through an energy lens, we introduce an approach (referred to as the “MicroFEWs approach”) that may assist remote communities in Alaska in making informed decisions regarding the use of renewable energy to increase FEW security. Our example uses the MicroFEWs approach to assess the impacts of increased renewable energy generation on FEW security in the community, more specifically to food security through potential changes to the community's fish processing industry. This approach can serve as a basis for investigating the FEW nexus in varying contexts and locales.