Residential Microgrid Research Articles

Cloud Assisted IoT-Operated Small Residential Microgrid with Collusive Model Approach for Energy Trading

This article explores a strategic approach for an energy-self-sufficient and economically sustainable small residential community, powered by rooftop solar photovoltaics. This community plays the role of a microgrid (MG) with a microgrid operator (MO). It operates in two distinct modes: within the MG (peer-to-peer or P2P) and with the utility grid. The P2P transfer is experimentally performed with open-source Internet of Things (IoT) applications from the cloud. This will enable a low-cost MG operation for developing countries. Residents from the community are elected considering their support towards community welfare, and they are considered as delegates. The MO with delegates, control the energy transfer operation. This approach distributes generated energy among community members at low prices if there is energy demand, minimizing carbon footprint. The action of selling energy by a prosumer during the need of fellow resident is considered as the token of social service towards the community. A social service counter (SSC) is chosen to identify services for each prosumer in the MG. When a seller sells energy within the community, the SSC increases. This count rewards the prosumer in several ways. MO only allows prosumers to participate in energy trading with the grid during the high-demand hours of the day. Delegates play an essential role in protecting the community's interest while selling energy outside the community. They try to form a coalition among participants to reduce installation costs and maximize the cumulative payoff. A comparative study between the proposed coordination game and two competitive game approaches, namely Cournot and Stackelberg's algorithm in the restricted domain, reveals that the proposed method is well suited for a small residential MG. Shapley value is a tool that identifies each delegate's contribution during the game. The paper employed this method for the overall coordination game to identify the most acceptable payoff for individual players.

Hybrid model for microgrid short term load forecasting based on machine learning

Short-term load forecasting (STLF) is crucial for microgrid (MG) operators to optimize energy generation and storage schedules based on anticipated load variations. Accurately predicting peak demand periods enables operators to ensure sufficient power supply while minimizing reliance on expensive backup sources, resulting in cost savings, and improved overall system efficiency. Residential MG power demand is highly dynamic due to external factors like residents’ lifestyles, behaviors, and weather responses, leading to significant irregularity and management challenges. To deal with these challenges, we propose a hybrid STLF model that combines Artificial Neural Networks (ANN) and Fuzzy Logic (FL), referred to as the Adaptive Neuro-Fuzzy Inference System (ANFIS). This model was trained and tested using real power consumption data. We evaluated the performance of the ANFIS model by comparing it with another ANN model using three evaluation metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The trained ANFIS model achieved an MAPE of 8.7528% and an RMSE of 0.4752kw, while the ANN model achieved an MAPE of 8.8123% and an RMSE of 0.4816kw. These results confirm the accuracy of the hybrid ANFIS model compared to ANN. The ANFIS model demonstrated its ability to capture complex and nonlinear relationships between various factors affecting load demand, making it suitable for handling the dynamic nature of MG load.

Energy management of the residential smart microgrid with optimal planning of the energy resources and demand side

The study specifically aimed to model the optimal operation of these appliances based on their usage patterns, rather than relying on the capacity of demand flexibility in demand response (DR) and energy pricing. The modeling operation of the appliances is done using two-layer energy optimization. In this optimization, energy consumption by appliances is reshaped via DR and load shifting in first-layer optimization. Then, minimizing the consumption costs and consumers’ discomfort in the second layer is formulated with consideration of the optimized consumption from the first layer. The lp-metric method is employed to solve the proposed optimization in the GAMS software. Finally, the efficiency of the two-layer optimization is confirmed using testing proposed case studies in the numerical simulation.

Self-Healing Photovoltaic Microconverter With Zero Redundancy and Accurate Low-Cost Fault Detection

This paper studies a zero-redundancy fault-tolerant dc-dc series resonant converter (ZR-SRC) for residential photovoltaic (PV) applications. This converter can handle faulty conditions on-the-fly using topology morphing control without increasing the number of components. Furthermore, it can withstand a single fault in both input and output bridge semiconductors, i. e., up to two semiconductor faults. The proposed dc-dc converter could increase the availability of residential photovoltaic-powered dc microgrids. Comprehensive fault diagnosis and localization algorithms are proposed for MOSFETs on both sides of the ZR-SRC. The paper proposes а short-circuit fault (SCF) detection in the input-side MOSFETs using the existing PV voltage measurements, decreasing the converter cost and size. The open-circuit fault (OCF) in the output-side MOSFETs is detected by sensing the voltage of the bottom two switches sharing the same ground with the dc bus. In addition, the post-fault operation of the converter is thoroughly analyzed. Recommendations for the PV power clipping are given to ensure the safe operation of the converter after a fault. A 300 W experimental setup is built to demonstrate the feasibility and performance of the proposed fault-tolerant converter.

Advanced Control Technique for Optimal Power Management of a Prosumer-Centric Residential Microgrid

Advanced Control Technique for Optimal Power Management of a Prosumer-Centric Residential Microgrid

Techno-Economic Analysis of Decentralized Residential Microgrid With Optimal Prosumer Selection

Techno-Economic Analysis of Decentralized Residential Microgrid With Optimal Prosumer Selection

Review on Non-Isolated Multiport Converters for Residential DC Microgrids

Nowadays, energy sustainability needs drive the development of novel power system architectures that efficiently harvest and deliver green energy. Specifically, DC Microgrids (DC-MG) have emerged as promising bases for distributed power generation, especially in residential applications. The pivotal role of power conversion and the need for more affordable and compact converters has led to an increasing research interest. MultiPort Converters (MPCs) exhibit beneficial operational characteristics for these applications and, therefore, a plethora of different topologies is suggested in the literature. Even though there have been some attempts to organize and review the field status, the categorization is based on the existence or not of isolation between the converter’s ports, without providing insight on the topology conception. In this article, a literature review is conducted to specify the most suitable non-isolated MPC topologies for residential DC-MGs. Converters with a power rating ranging from 0.1 to 1 kW are compared based on technical features and categorized according to their topology derivation process. This procedure is performed separately for MPCs suitable for unipolar and bipolar DC Buses. The selected approach highlights the design basis for each MPC in a structured manner, facilitating further development of original converters by both new and experienced researchers.

Peer-to-peer energy sharing model considering multi-objective optimal allocation of shared energy storage in a multi-microgrid system

A novel peer-to-peer (P2P) energy sharing model incorporating shared energy storage (SES) is proposed in order to effectively utilize renewable energy sources and facilitate flexible energy trading among microgrids. The model is divided into three main blocks. In the first block, a multi-objective optimal allocation scheme for SES is developed to maximize SES benefits, minimize carbon emissions, and enhance the sustainability of the energy sharing zone (ESZ). The second block introduces a new internal pricing mechanism based on the energy supply and demand ratio. Further, an innovative optimal scheduling strategy for the ESZ is presented in the third block, which includes demand response strategies for residential microgrids and control strategies for SES, with the goal of minimizing the energy cost of a multi-microgrid system (MMS). To solve the proposed complex and constrained model, a multi-objective slime mould algorithm based on generalized opposition-based learning and reference points is proposed. Simulation results demonstrate that the introduction of SES and energy sharing can reduce MMS's energy cost by 89.45 % and increase the sustainability index of ESZ by 9.08 %. The proposed P2P trading mechanism can strictly lock the internal trading prices within the grid prices, which fully mobilizes the microgrids to participate in P2P trading.

Photovoltaic-Based Residential Direct-Current Microgrid and Its Comprehensive Performance Evaluation

The “dual carbon” strategy has drawn attention to distributed PV systems for their flexibility and variability, but the rising need for direct-current (DC) loads on the load side has created additional difficulties for microgrid system upgrades. In this article, a PV-based microgrid design approach for residential buildings is suggested, working on the assumption that distributed PV systems are given top priority to handle domestic DC needs. The residential DC microgrid system’s overall design concept is first put out, and the circuit system is then concentrated to supply the main idea for the ensuing verification of the system’s viability. Secondly, the actual power generation in the selected area was clarified by testing, and then the electricity consumption of DC loads accounted for about 20.03% of the total power consumption according to the survey of 100 users. In addition, the circuit system is subjected to spectral model measurements and physical measurements to verify the operational performance of the circuit system; the feasibility of the PV microgrid system is further verified using dual testing of the PV system and the circuit system. The test results show that the proposed DC microgrid system can accurately provide the required voltage for small household DC appliances, such as 24 V, 14 V, 5 V, etc. Finally, the system economics were analyzed, and the equipment payback years were estimated. The supply and demand of PV power generation and DC appliances can be balanced via the construction of a microgrid. This study offers a fresh concept for the use of PV technology. The concept behind this research can serve as a model for the creation and application of other new energy sources.

Design optimization of a grid-tied microgrid for a residential community in southern Bangladesh

Abstract Growing energy demand, diminishing fossil fuel reserves and geopolitical tensions are serious concerns for any country’s energy strategy and security. These factors have a greater impact on developing countries, as many of them rely largely on traditional energy resources. Cleaner energy generation is the viable alternative for mitigating these problems, as well as achieving energy independence and tackling climate change. The article discusses planning and design optimization of a residential community microgrid based on multiple renewable resources. In particular, the design and techno-economic assessment of a grid-tied hybrid microgrid for meeting the electricity demand of an alluvial region, Urir Char, located in southern Bangladesh, was addressed. Hybrid Optimization of Multiple Energy Resources is used for the evaluation and it is supplemented by a fuzzy-logic-based load profile design strategy. In addition to the analysis, a predictive load-shifting-based demand management is also introduced. Several cases were considered for the studies and, after considering several criteria, a grid-tied system comprising a photovoltaic array, wind turbine and energy storage system was found to be the best fit for powering the loads. The suggested system reduces the life-cycle cost by 18.3%, the levelized cost of energy by 61.9% and emissions by 77.2% when compared with the grid-only option. Along with the microgrid design, cooking emissions and energy categorization were also discussed.

Impact of intra-hourly scheduling on state of health of battery for electric vehicle integration in smart residential microgrid

Impact of intra-hourly scheduling on state of health of battery for electric vehicle integration in smart residential microgrid

Peer-to-peer trading price and strategy optimization considering different electricity market types, tariff systems, and pricing models

This study investigated the peer-to-peer (P2P) trading price and strategy based on market-driven methods within a residential microgrid consists of two prosumers (i.e., P1 and P2) and one consumer (i.e., C1), which establishes a diverse microgrid trading market in terms of peer characteristics (e.g., system configurations and electrical load profile). Five cases are proposed in this study considering different electricity market types (i.e., peer-to-grid and P2P markets), retail tariff systems (i.e., progressive and time-of-use (ToU) tariff systems) as well as market-driven P2P trading pricing models (i.e., uniform and individual pricing models). Peer electrical load data are collected from three typical four-person households in Hong Kong along with their domestic electrical appliance utilization pattern. The results indicate that the individual pricing model has led to the dynamics of P2P electricity trading price than the uniform pricing model, and P2P trading is more economically efficient under the ToU tariff system. The maximum electricity bill saving of the entire microgrid can be achieved by 31.33% in summer and by 43.02% in winter. Besides, it is observed that the installation of battery energy storage system (BESS) has facilitated the self-consumption ratio of the renewable energy system to 91.98% in summer and 100% in winter. This implies that the BESS plays a pivotal role in improving the flexibility in managing the P2P trading strategy, and enhancing the efficiency in electricity dispatching within the microgrid. This study contributes to the novelty in science since it provides a comprehensive framework that can adapt to changing market conditions.

A Novel Hybrid Imperialist Competitive Algorithm–Particle Swarm Optimization Metaheuristic Optimization Algorithm for Cost-Effective Energy Management in Multi-Source Residential Microgrids

The integration of renewable sources and energy storage in residential microgrids offers energy efficiency and emission reduction potential. Effective energy management is vital for optimizing resources and lowering costs. In this paper, we propose a novel approach, combining the imperialist competitive algorithm (ICA) with particle swarm optimization (PSO) as ICA-PSO to enhance energy management. The proposed energy management system operates in an offline mode, anticipating data for the upcoming 24 h, including consumption predictions, tariff rates, and meteorological data. This anticipatory approach facilitates optimal power distribution among the various connected sources within the microgrid. The performance of the proposed hybrid ICA-PSO algorithm is evaluated by comparing it with three selected benchmark algorithms, namely the genetic algorithm (GA), ICA, and PSO. This comparison aims to assess the effectiveness of the ICA-PSO algorithm in optimizing energy management in multi-source residential microgrids. The simulation results, obtained using Matlab 2023a, provide clear evidence of the effectiveness of the hybrid ICA-PSO algorithm in achieving optimal power flows and delivering substantial cost savings. The hybrid algorithm outperforms the benchmark algorithms with cost reductions of 4.47%, 14.93%, and 26% compared to ICA, PSO, and GA, respectively. Furthermore, it achieves a remarkable participation rate of 50.6% for renewable resources in the energy mix, surpassing the participation levels of the ICA (42.88%), PSO (40.51%), and GA (38.95%). This research contributes to the advancement of power flow management techniques in the context of multi-source residential microgrids, paving the way for further research and development in this field.

Energy management of multi-microgrids with renewables and electric vehicles considering price-elasticity based demand response: A bi-level hybrid optimization approach

This article develops an energy management scheme for multi-microgrid systems involving the scheduling strategy of distributed resources, renewables and plug-in electric vehicles penetration to enhance the economic-environmental benefits of the system. The proposed framework also integrates the price elasticity-based augmented demand response program into the energy management (EM) problem to investigate the influence of dynamic energy pricing and incentives provided to consumers. The local power trading between adjacent microgrids and external trading with the primary grid through each microgrid's generation resources scheduling facilitates the microgrids’ cost reduction while maintaining supply-demand balance in the system. But, the ubiquitous intermittent nature of renewables, load demands, and vehicle charging imply complexities in the system. Hence, this work formulates the EM problem incorporating the uncertainties of renewables and load demands by the worst-case realization and employs a bi-level hybrid grey wolf-whale optimization; the first level is optimized from the microgrid's standpoint to obtain the dynamic pricing, and the load demands modification, whereas the operational cost of the multi-microgrid system and the utility profit are optimized in the second level. The proposed approach is studied on a test system consisting of residential, commercial and industrial microgrids; substantial simulation results are presented to validate the effectiveness.

Uncertainty aware optimal battery sizing for cloud energy storage in community microgrid

Cloud energy storage systems (CES) are a new paradigm for the application of consumer-side energy storage in residential community microgrids. By transforming traditional consumers into self-sustaining and utility consumers, CES facilitates interaction between consumers and utilities as well as between consumers. Residential CES development is complicated by the flexible capacity of CES batteries and the uncertainty of supply. This paper recommends the CES system for residential prosumers. A machine learning-based uncertainty quantization and an artificial ecosystem optimization (AEO) method are used to determine optimal battery capacity considering PV, load, and price uncertainty. Compare this algorithm to the other four optimization algorithms to find out how well it works. The feasibility and profitability of deploying CES with residential PV are assessed. This problem minimizes the user’s cost and maximizes the profit of the CES operator. The sensitivity analysis of CES is analyzed for various storage capacity penetrations. Further, optimal battery capacity determines the use of an AEO algorithm. Based on a PJM dataset of residential PV, load, and electricity price, simulation results demonstrate that the suggested framework integrated with a distributed PV system is more economical to DES.

Hierarchical control of networked microgrid with intelligent management of TCLs: A case study approach

Significant integration of Renewable Energy Sources (RES) has led to the evolution of an alternative platform with distributed generation and clustered loads in low or medium-voltage distribution networks. However, a standalone microgrid is susceptible to several inconsistencies that may lead to system failure and disruption due to the inherent uncertainty of RES and its intermittent nature. The paper proposes a coordinated and hierarchical control framework with an inverter-fed primary controller to maintain the nominal voltage and frequency with a regulated exchange of ancillary power in networked microgrids. Further, during peak hours, a secondary control mechanism is incorporated through Demand Side Management (DSM). Intelligent aggregation of TCLs has been done based on decision tree models and further controls to maintain the nominal frequency in networked microgrids adhering to IEC 60929 considering different temperature ranges (16–22) °C. A thorough investigation is carried out considering two separate microgrids of critical and non-critical loads respectively (one a hospital microgrid and another residential microgrid) connected through an interlinking converter. The modeling of the test system and analysis of the proposed control mechanism is simulated in MATLAB SIMULINK and real-time validation in the OPAL-RT platform considering several dynamics.

Parametric study on utilization of PV-energy in residential microgrids with hot water heating

The seamless coexistence of distributed photovoltaics (PV) with the utility network may be maintained by focusing on higher energy autonomy of micro-grids.This may be approached by seeking any opportunities to increase local energy consumption, especially when legal regulations are cutting-down the benefits for energy overproduction.The microgrids combining PV with electric storage and heat pumps largely fail at satisfying local energy needs autonomously in winter periods and create excessive load of power network. Although the all-electric microgrid is a long term target, any improvements to the existing residential infrastructure with mixed-supply sources are worth revisiting as low-cost steps towards more conscious energy utilization.The concept of using Domestic Hot Water (DHW) heating to dump locally the excess of PV-energy is affordable method to improve energy autonomy of residential microgrids with minimal interference to their existing configuration and the advantage of no additional energy demand.The use of PV for DHW heating was studied in various configurations, but in contrast to former works, this paper presents the parametric analysis focusing on increasing the local consumption (goal A), minimizing the interference with utility grid (goal B) and optimal adjustment to net-metering accounting rules (goal C).The case study is the residential-type micro-grid in climate of Central Europe. The study uses both simulated (energy consumption profiles) and recorded (solar irradiance) data. The simulation is based on microgrid proportions and provides the results beyond reach of single experiments and conclusions applicable to similar cases, regardless the absolute size.

Optimal scheduling of CCHP-based resilient energy distribution system considering active microgrids' multi-carrier energy transactions

This paper introduces a two-stage two-level optimization method for optimal day-ahead and real-time scheduling of multicarrier energy distribution systems and microgrids. The model considers the incentive-based and price-based demand response programs to encourage microgrids to transact electrical, heating, and cooling energy carriers with the energy distribution system, which is named hereafter as the energy system. Further, the model formulates the resilient operation of the energy system considering the energy transactions with the electrical, heating, and cooling markets. The main contribution of this paper is the integration of demand response procedures of microgrids in energy transactions with the energy system considering the switching of electrical switches and heating and cooling control valves. The optimization process is another contribution of this paper that is decomposed into two stages that consist of day-ahead and real-time horizons. The first stage is also decomposed into two levels that determine the optimal scheduling of the energy system and microgrids in day-ahead markets. The second stage is comprised of two levels that commit the energy system and microgrids resources. A resiliency index is proposed to assess the resiliency of the energy system in shock conditions. The proposed method was simulated for the 123-bus test system. Different types of microgrids, incentive-based and price-based demand response processes were considered. Simulation results confirmed that the proposed method can reduce the costs of residential, industrial, and commercial microgrids by about 4.47%, 3.88%, and 5.47% concerning only the real-time pricing process. Further, the model can increase the aggregated benefits of the energy system in the day-ahead and real-time markets by about 0.608 Million Monetary Units (MMUs) and 1.10 MMUs, respectively.

Design of a Small-Scale Domestic Microgrid

This article presents a simulation of an isolated residential electrical Micro-Grid (MR) that incorporates distributed generation technologies such as photovoltaics, battery energy storage, and electric vehicle systems. The research focuses on the major loads found in residential installations and the load preferences of different users. To optimize energy usage, a new Energy Management System (EMS) was developed and implemented. The EMS utilizes predefined criteria and parameters to determine the most suitable energy source for a given time and regulates the operation of loads based on energy availability. The article also delves into the design and sizing of electricity-supplying elements for household loads within the microgrid. To validate the proposed system, prototype elements were tested using an isolated residential microgrid emulator. The emulator proved to be a valuable academic and research tool, enabling multiple experiments and tests in the field of isolated microgrids. It facilitated a better understanding of calculations and theoretical approaches while providing a platform for validation. Overall, this simulation and the accompanying emulator offer significant insights into the operation and optimization of isolated residential microgrids. They contribute to the advancement of research in this field and help in developing efficient and sustainable energy systems for residential communities.

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.