Residential Microgrid Research Articles

Energy interactions between in-home energy management (i-HEM) systems for enhancing resilience in smart residential microgrid

Energy interactions between in-home energy management (i-HEM) systems for enhancing resilience in smart residential microgrid

A safe reinforcement learning-based charging strategy for electric vehicles in residential microgrid

With the growing popularity of electric vehicles (EVs), it is a new challenge for the residential microgrid system to conduct charging scheduling to meet the charging demands of EVs while maximizing its profit. In this work, a safe reinforcement learning (RL)-based charging scheduling strategy is proposed to meet this challenge. We construct a complete microgrid system equipped with a large charging station and consider different types of EVs, as well as the vehicle-to-grid (V2G) mode and nonlinear charging characteristics of EVs. Subsequently, the charging scheduling problem is formulated as a constrained Markov decision process (CMDP) due to the various limitations of power and demands. To effectively capture the uncertainties of the supply side and demand side of the microgrid, a model-free RL framework is employed. However, the curse of dimensionality of the action space is inevitable as EVs increase. To solve this problem, a charging and discharging strategy based on a general ladder electricity pricing scheme is designed. Different EVs are divided into different sets according to their states under this strategy, and the agent gives control signals of different sets instead of controlling each EV individually, which effectively reduces the dimension of the action space. Subsequently, a constrained soft actor-critic (CSAC) algorithm is designed to solve the established CMDP, and a safety filter is introduced to ensure safety. In the end, a numerical case is conducted to verify the effectiveness of the proposed method.

Overcoming the Fluctuating Curve from PV Power Generation by Modelling the Integration of Hydrogen Energy Storage Into Electricity Microgrid

Java Madura Bali (Jamali) system is the Indonesian largest grid, which contributes approximately 61%, with the Coal Thermal dominates by 59% and followed by the New Renewable Energy (NRE) of 9%. Solar Photovoltaic Power Plants (SPP) contribute only 1% from the NRE itself. Low voltage customers (TR) are approximately 61% with the residential contribute approximately 42%. The rates of the national SAIDI is 12.72 hours/subscriber/year and the national SAIFI is 9.25 times per subscriber/year. There is some challenge in regulating voltage in area with high penetration of solar PV, case of voltage fluctuation in response to the fluctuating of solar irradiance, as well as other factors such as load transient. This voltage fluctuation is proportional to the amount of current generated by the PV system and also proportional to the solar irradiance.Hydrogen energy storage (HES) is an alternative to NRE-based energy storage technology. Integrating solar PV with hydrogen energy storage and optimisation device may mitigate PV-driven voltage fluctuation, preventing excessive reserve power flow on the grid. The hydrogen energy storage system can cover variation of the loads at the nearby solar PV system in the microgrid and the ability to manage those loads, as mentioned in the energy management system. Integrating the HES within Solar PV system can improve the curve shape, improve system reliability and voltage stability, especially in the Residential Microgrid. Also may build the opportunities to earn new revenues from wholesale electricity markets, enhancing the reliability rate and competitive monetary value.

BESS Reserve-Based Frequency Support During Emergency in Islanded Residential Microgrids

Due to limited reserves and 100% inverter-based resources in an islanded residential microgrid (IRM), largefrequency oscillations may arise during load/generation fluctuations. As an independent grid-forming unit, a battery energy storage system (BESS) can participate in load-frequency control (LFC) to achieve environment-friendly and reliable supply in IRMs. The formation of an interconnected islanded residential microgrid (IIRM) system and coordinated active power interchange among IRMs can help overcome BESS units limited reserve constraints. In this work, a reserve control (RC) level is proposed to be added in the conventional LFC architecture to provide frequency support to disturbance-affected areas with the help of a set of BESS units available reserve capacity. Following a significant disturbance detected, the RC level is activated based on their pre-defined contracted demands and net generation change information. The RC level also considers real-time monitoring of each microgrid's active power reserve to avoid frequency instability during emergency conditions. System performance with the proposed LFC model is analyzed with extensive tests conducted on a CIGRE TF C6:04:02 benchmark IIRM system. The proposed algorithms are also validated using a real-time digital simulator with hardware-in-the-loop setup.

Supercapacitors Fast Ageing Control in Residential Microgrid Based Photovoltaic/Fuel Cell/Electric Vehicle Charging Station

The demand for microgrids and their applications in buildings, industries and for very specific applications is increasing over time. Most of these microgrids are dependent on renewable energy sources, which brings along problems of intermittent energy production. To maintain the balance of the grid, normally storage devices are used. Supercapacitors (SCs) are emerging as one of the potential solutions to solve the issue of intermittent energy production by renewable sources because of their high-power densities and rapid charge/discharge capability. In other terms, SCs can charge, and discharge rather quickly as compared to traditional lithium-batteries. This usage makes it interesting for optimizing decentralized energy generation-based PV systems operations. In this paper, the authors propose the supercapacitors fast ageing control in residential microgrid, including electric vehicle charging station based Photovoltaic-Fuel Cell system. Supercapacitors fast ageing control concept focuses on keeping the electrical parameters of the SCs around the optimal operation points by smoothing the power fluctuations in the system. The used SCs model is essentially based on the intermittent current waveforms along with variable temperature conditions. It enables us to describe the degradation of the supercapacitor’s parameters based on the effects of the temperature and the DC-current undulation. To maintain the electrical parameters of SCs around the optimal operation points, the authors propose a new control based on maintaining the SCs resistance at a minimal level and its capacitance at a maximal level by adjusting the SCs current control according to the optimal operating points tracking. The results validate the effectiveness of the approach, this is important because controlling the fast degradation of capacitance optimizes the lifetime of SCs system. Future research may explore scalability to large microgrids and integration with diverse renewable energy systems.

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

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

V2V Energy Trading in Residential Microgrids Considering Multiple Constraints via Bayesian Game

The V2V (vehicle-to-vehicle) energy swapping strategy provides an alternative charging for electric vehicles (EVs) to alleviate the problem of overload caused by the un-coordinately charges in residential microgrids (MGs) during the peak. However, stochastic factors of EVs and neglected network losses cause inflexibility and impracticality of the existing methods. Focused on the stochastic factors of energy trading caused by EVs and network constraints in residential MGs scenario, a V2V Energy Trading in Residential Microgrids (VETRM) considering multiple constraints via Bayesian game model is proposed. Frist, the Bayesian game modeled types of players by information including the stochastic characteristics of EVs, which results in uncertainties that the game participants determine the roles of seller or buyer that depends on the states of power surplus or lack. Then, utility functions based on the sensitivity analysis to the impact of V2V transactions on the network and to guarantee the exchange of energies to inviolate network constraints are established. The solution of model based on game equilibrium has been rigorously derived. Two scenarios fulfill the comparisons of existing models and proposed model for simulation including IEEE 33 and IEEE 123 bus system that the proposed model improve the revenues of EV in reducing energy loss and smoothing the peak of the power system to demonstrate the effectiveness of the VETRM model.

Optimal Power Scheduling and Techno-Economic Analysis of a Residential Microgrid for a Remotely Located Area: A Case Study for the Sahara Desert of Niger

The growing demand for electricity and the reconstruction of poor areas in Africa require an effective and reliable energy supply system. The construction of reliable, clean, and inexpensive microgrids, whether isolated or connected to the main grid, has great importance in solving energy supply problems in remote desert areas. It is a complex interaction between the level of reliability, economical operation, and reduced emissions. This paper investigates the establishment of an efficient and cost-effective microgrid in a remote area located in the Djado Plateau, which lies in the Sahara Ténéré desert in northeastern Niger. Three cases are presented and compared to find the best one in terms of low costs. In case 1, the residential area is supplied by PVs and a battery energy storage system (BESS), while in the second case, PVs, a BESS, and a diesel generator (DG) are utilized to supply the load. In the third case, the grid will take on load-feeding responsibilities alongside PVs, a BESS, and a DG (used only in scenario 1 during the 2 h grid outage). The central objective is to lower the cost of the proposed microgrid. Among the three cases, case 3, scenario 2 has the lowest LCC, but implementing it is difficult because of the nature of the site. The results show that case 2 is the best in terms of total life cycle cost (LCC) and no grid dependency, as the annual total LCC reaches about $2,362,997. In this second case, the LCC is 11.19% lower compared to the first case and 5.664% lower compared to the third case, scenario 1.

Novel V2G regulation scheme using Dual-PSS for PV islanded microgrid

The incorporation of electric vehicles (EVs) into residential/industrial microgrids (MGs) presents some limitations such as fluctuations in voltage, frequency, and lack of power supply. According to the literature, the control methods for vehicle-to-grid (V2G) in MGs are insufficiently coordinated for charging and regulation, limited by narrow charging control constraints, complex, and computationally demanding. Moreover, the power system stabilizer (PSS) is employed only in the generation side. To this regard, a PSS is proposed in this paper along with the proportional-integral-derivative (PID) controller, which is tuned adaptively using Harris hawks optimizer (HHO) for frequency, voltage, and power-sharing regulations through the parked EVs. The considered islanded MG is consisting of a diesel generator as a backup generation source, dealing with the intermittent nature of the solar photovoltaic (PV) plant and residential load. It also includes an EV aggregator. In case of partial shading of the solar PV farm and triggering the load demand, the proposed control strategy provides a complementary signal to promote short response time during transients, boost the transient stability performance, and offer damping characteristics adequate for V2G regulation with the islanded residential MG. To examine the efficacy of the proposed scheme, a detailed simulation model using Matlab/Simulink was built. The final findings show that the proposed direct adaptive PID-PSS controller gives significantly enhanced voltage and frequency with spinning reserve compared to the conventional PI-controller both considering the inclusion of V2G on the MG regulation side and conventional controller without V2G and PSS on the regulation side. Also, it can provide power flow in a bi-directional way during the partial shading contingency that the residential MG may experience within 24 hr.

An optimal configuration method for microgrid second-use battery storage system considering users allocate the cost of peak-shaving auxiliary service

The second use of retired batteries can reduce energy storage system (ESS) costs, relieve the power supply pressure of the power grid during peak hours, peak cutting and valley filling and promote energy saving and emission reduction. Power users can allocate a certain amount of peak-shaving auxiliary service costs to guide users to cut peaks and fill valleys. This paper presented an optimal configuration method for a microgrid second-use battery energy storage system (BESS) considering the users allocate peak shaving auxiliary service costs. A second-use BESS optimization configuration model was established to configure the second-use BESS with the lowest cost and the lowest peak shaving service cost allocated by users. The optimization model was solved by the particle swarm algorithm (PSO). And compared with the configured conventional BESS of residential area micro-grid. Simulation is carried out on the MATLAB platform, and the results verify the model’s and algorithm’s rationality.

An Adaptive Staggered Investment Strategy for promotion of residential rooftop solar PV installations in India

Rooftop solar PV in India has seen good progress in the Commercial and industrial sectors, but the progress in the domestic sector is relatively slow due to the high initial installation cost. Thus, there arises the need for good market models for Rooftop Solar (RTS) implementation. This paper conducts a comparative study of workable RTS market models by employing the discounted cash flow method, as per the recent regulatory guidelines. Market models are formulated and tested for a typical residential high-rise apartment complex in India comprising 15 storied buildings with a combined maximum demand of 180kVA. The results suggest that the centralized community RTS model of 80kWp capacity with upfront financing is suitable when compared to the decentralized individual model, as it has the lowest levelized cost of 3.39 ₹/kWh and a payback period of 5.5 years. With the federal subsidy, the prosumer levelized cost reduces to 2.06 ₹/kWh with a payback period of 3.3 years. Thus grid parity is achieved for all tariff tier rates. With adaptive staggering strategy, this scheme is validated to be more attractive for the urban residential microgrids, as the solar installation of 80kWp and its cost can be staggered and even reduced over the planning period. Hence capital installation and operation costs can be distributed over the stipulated time interval. The study result gives RTS stakeholders insight into selecting the most cost-effective market model to suit their requirements. Financial analysis of the proposed models provides input to the customers, developers, and policymakers to assess the financial merit of adopting the suitable business model for RTS development. The proposed analysis can be replicated for high-rise residential buildings, especially in cities with high electricity tariffs. With time, a decrease in solar PV installation price and an increase in grid price are expected; hence, the overall investment cost gets reduced and staggered.

Optimal Power Flows Control for Home Energy Management With Renewable Energy and Energy Storage Systems

This article presents a home energy management system (HEMS) based on the proposed adaptive artificial bee colony (AABC) optimization method, of managing a residential microgrid connected to the main grid. The studied microgrid consists of a photovoltaic system (PVS), a battery storage system (BSS), and electricity loads. The proposed AABC-based HEMS optimizes power flow ratio among PVS, BSS, and the main grid with respect to the load demand and electricity rate in order to minimize electricity consumption cost. Three dc-dc converters are actively controlled with an active dual-loop control architecture to dominate the power distribution among multi-power sources. The performances of the proposed optimal power flows control (PFC) under different electricity load levels and PVS generation conditions are examined. In the PFC experiments, the designed HEMS is implemented on a digital signal processor. The experimental results demonstrate the effectiveness and superiority of the proposed AABC-based HEMS compared with the rule-based control strategy and the equivalent consumption minimization strategy in terms of electricity consumption cost.

Guest editorial: Application of cloud energy storage systems in power systems

Guest editorial: Application of cloud energy storage systems in power systems

주거용 열과 전기에너지 공급을 위한 마이크로터빈, 태양광및 풍력발전의 최적 조합에 관한 연구

This study assumes a residential thermal and electric microgrid with distributed energy resources (DERs) composed of microturbines (MTs), photovoltaic systems (PVs), and wind turbines (WTs). The objective of this study is to find the optimal combination of MTs, PVs, and WTs for residential customers. For this purpose, this study defines the objective function to minimize not only the generation costs of electric, thermal energy, MTs, PVs, and WTs but also the voltage variations of the grid. Since HOMER can evaluate the lifecycle operating and maintenance costs of thermal and electric grids, this study evaluates the lifecycle cost functions of these DERs. If the cost function is fitted by second-order polynomials, quadratic programming (QP) can be used for an optimization problem because the cost function of QP is defined in the second order. Thus, using QP implemented in MATLAB, the optimal combination (e.g., the most effective capacity) of MTs, PVs, and WTs is determined. To validate the proposed method, an electric and thermal residential grid is modeled by the IEEE 13-bus test feeder. Using the backward and forward sweep power-flow method, the voltage variation of the test feeder enhanced by the DERs is evaluated. Next, cost functions and voltage variations are used as input to QP. A case study with the test feeder demonstrates the effectiveness of the proposed method.

Proof of Work Consensus Based Peer to Peer Energy Trading in the Indian Residential Community

Rooftop solar power generation is becoming more widespread in residential microgrids. As well as new concepts of electricity markets, such as peer-to-peer (P2P) markets, where consumers and prosumers can directly exchange locally generated energy with each other without any intermediary third party for sustainable development. Data security is a big concern with energy trading; therefore, blockchain technology is being used more and more in energy markets. It has the potential to simplify P2P energy trading. In this paper, blockchain is designed to fit into the decentralized nature of the P2P market, securing the payment mechanism and transaction data store. The blockchain-enabled platform is developed using the Proof-of-Work (PoW) consensus algorithm, and is verified with the help of the Postman application programming interface (API). All transactions involving the buying and selling of energy are handled by a miner without the help of any third parties. The study of a five-user residential community, whether the strategy is recommended or not, is validated through simulation findings. An overview of the results revealed that all users benefited from the developed, secure P2P platform.

Demand Response Management of a Residential Microgrid Using Chaotic Aquila Optimization

In this paper, Chaotic Aquila Optimization has been proposed for the solution of the demand response program of a grid-connected residential microgrid (GCRMG) system. Here, the main objective is to optimize the scheduling pattern of connected appliances of the building such that overall user cost are minimized under the dynamic price rate of electricity. The GCRMG model considered for analysis is equipped with a fuel cell, combined heat and power (CHP), and a battery storage system. It has to control and schedule the thermostatically controlled deferrable and interruptible appliances of the building optimally. A multipowered residential microgrid system with distinct load demand for appliances and dynamic electricity price makes the objective function complex and highly constrained in nature, which is difficult to solve efficiently. For the solution of such a complex highly constrained optimization problem, both Chaotic Aquila Optimization (CAO) and Aquila optimization (AO) algorithms are implemented, and their performance is analyzed separately. Obtained simulation results in terms of optimal load scheduling and corresponding user cost reveal the better searching and constrained handling capability of AO. In addition, experimental results show that a sinusoidal map significantly improves the performances of AO. Comparison of results with other reported methods are also made, which supports the claim of superiority of the proposed approach.

Energy management in residential microgrid using model predictive control-based reinforcement learning and Shapley value

This paper presents an Energy Management (EM) strategy for residential microgrid systems using Model Predictive Control (MPC)-based Reinforcement Learning (RL) and Shapley value. We construct a typical residential microgrid system that considers fluctuating spot-market prices, highly uncertain user demand and renewable generation, and collective peak power penalties. To optimize the benefits for all residential prosumers, the EM problem is formulated as a Cooperative Coalition Game (CCG). The objective is to first find an energy trading policy that reduces the collective economic cost (including spot-market cost and peak-power cost) of the residential coalition, and then to distribute the profits obtained through cooperation to all residents. An MPC-based RL approach, which compensates for the shortcomings of MPC and RL and benefits from the advantages of both, is proposed to reduce the monthly collective cost despite the system uncertainties. To determine the amount of monthly electricity bill each resident should pay, we transfer the cost distribution problem into a profit distribution problem. Then, the Shapley value approach is applied to equitably distribute the profits (i.e., cost savings) gained through cooperation to all residents based on the weighted average of their respective marginal contributions. Finally, simulations are performed on a three-household microgrid system located in Oslo, Norway, to validate the proposed strategy, where a real-world dataset of April 2020 is used. Simulation results show that the proposed MPC-based RL approach could effectively reduce the long-term economic cost by about 17.5%, and the Shapley value method provides a solution for allocating the collective bills fairly.

Novel Universal Power Electronic Interface for Integration of PV Modules and Battery Energy Storages in Residential DC Microgrids

This paper introduces the novel concept of a highly versatile smart power electronic interface for fast deployment of residential dc microgrids. The proposed approach has bidirectional power flow control capabilities, wide operating voltage range, and high efficiency resulting from the topology morphing control utilization. This enables universal compatibility with the majority of the commercial 60- and 72-cell photovoltaic modules, as well as the efficient charge/discharge control of the 24 V and 48 V battery energy storages using the same hardware platform. The proposed concept features fully autonomous operation where switching between the photovoltaic and battery interfacing modes is automatically done using the input source identification algorithm. Moreover, the proposed universal interface converter employs droop control and solid-state protection, making it fully compatible with the emerging standards and requirements for power electronic systems used in dc microgrid environments. A 350 W prototype was developed and tested in the residential 350 V dc microgrid with droop control to validate the proposed concept experimentally.

Average Current Control with Internal Model Control and Real-time Frequency Decoupling for Hybrid Energy Storage Systems in Microgrids

Among hybrid energy storage systems (HESSs), battery-ultracapacitor systems in active topology use DC/DC power converters for their operations. HESSs are part of the solutions designed to improve the operation of power systems in different applications. In the residential microgrid applications, a multilevel control system is required to manage the available energy and interactions among the microgrid components. For this purpose, a rule-based power management system is designed, whose operation is validated in the simulation, and the performances of different controllers are compared to select the best strategy for the DC/DC converters. The average current control with internal model control and real-time frequency de-coupling is proposed as the most suitable controller according to the contemplated performance parameters, allowing voltage regulation values close to 1%. The results are validated using real-time hardware-in-the-loop (HIL). These systems can be easily adjusted for other applications such as electric vehicles.

A novel deep reinforcement learning based business model arrangement for Korean net-zero residential micro-grid considering whole stakeholders’ interests

In this paper, we put forward a deep reinforcement learning (DRL) based energy management system (EMS) solution for a typical Korean net-zero residential micro-grid (NZR-MG). We model NZR-MG EMS to extract a profitable business model that respects whole stakeholders’ interests and meets Korean power system regulations and specifications. We deployed the value-based DRL technique, dual deep Q-learning (DDQN), as a solution for our EMS problem since of its simplicity, stability in the learning process, and non-dependency on hyper-parameter selection compared to actor–critic methods. Due to the implementation of mixed-integer nonlinear programming (MINLP) to solve the reward function in this paper, DDQN, despite other DRL methods, provides precise, explicit, and meaningful rewards. In addition to encouraging the agent to choose profitable actions, this approach releases the proposed DRL-based method from the hindrance of redesigning the reward function experimentally in any future extension of the environment elements. Moreover, attaching transfer learning (TL) to the process of training DDQN agent defeat the MINLP imposed latency in training convergence. An extensive benchmark is proposed to test the superiority of the proposed method versus other DRL algorithms.