Residential Microgrid Research Articles

Optimising Building-to-Building and Building-for-Grid Services Under Uncertainty: A Robust Rolling Horizon Approach

Energy systems are undergoing radical changes that have resulted in buildings being regarded as proactive players with the potential to contribute positively to energy networks. This study investigates the role of active buildings (ABs) as prosumers in energy systems by introducing a building-to-building (B2B) strategy for energy exchange between residential units, as well as a building-for-grid (B4G) model by exploiting the demand flexibility of residential microgrids (RMGs). The mid-market rate mechanism is adopted to produce local market price signals at RMG level. A robust rolling horizon controller is developed for real-time energy management of a community of ABs. This control philosophy can improve the robustness of the RMG in face of real-time weather and energy price prediction errors. The proposed method is a multi-level optimisation which pursues multiple goals while making a trade-off between operational cost and occupant comfort. Finally, the repercussions of COVID-19 induced power consumption resulting from changing lifestyle and building occupancy profile is analysed by the proposed method as a case study. The results show that the proposed B2B and B4G strategy can reduce energy bills by 18.45%, while notable robust real-time control and computational efficiencies are achieved when benchmarked against conventional methods.

A Model Predictive Control-Based Decision-Making Strategy for Residential Microgrids

This work presents the development of a decision-making strategy for fulfilling the power and heat demands of small residential neighborhoods. The decision on the optimal operation of a microgrid is based on the model predictive control (MPC) rolling horizon. In the design of the residential microgrid, the new approach different technologies, such as photovoltaic (PV) arrays, micro-combined heat and power (micro-CHP) units, conventional boilers and heat and electricity storage tanks are considered. Moreover, electricity transfer between the microgrid components and the national grid are possible. The MPC problem is formulated as a mixed integer linear programming (MILP) model. The proposed novel approach is applied to two case studies: one without electricity storage, and one integrated microgrid with electricity storage. The results show the benefits of considering the integrated microgrid, as well as the advantage of including electricity storage.

Enhancement of battery life in microgrid energy management using mixed integer linear programming and hybrid knapsack

Microgrid with distributed energy resources and energy storage system provides sustainability and resiliency. In this research, residential community microgrid is examined with responsive loads that create flexible generation-demand model. An optimization algorithm using mixed integer linear programming (MILP) has been formulated to minimize the operating cost and emission of dispatchable power generation, with the help of demand response. Usually, in renewable energy–based grid-connected microgrid, the batteries are managed under partial state of charge (SoC) conditions due to the limit of power imported from grid. The proposed MILP model ensures full SoC operation and safe charging or discharging dynamics of the battery in order to enhance its lifespan. Moreover, the day-ahead scheduling of household appliances is carried out using a novel hybrid knapsack method, which combines binary and fractional knapsack algorithms. An electric vehicle battery is considered as a flexible power load, which offers an unique way of approach in scheduling of appliances. The results confirm that the power demanded by the appliances is fulfilled at the user-specified hour for maximum comfort along with minimum operating cost of microgrid. Generic algebraic modeling system (GAMS) tool is used to run the proposed algorithms.

Electrical demand aggregation effects on the performance of deep learning-based short-term load forecasting of a residential building

Modern power grids face the challenge of increasing renewable energy penetration that is stochastic in nature and calls for accurate demand predictions to provide the optimized power supply. Hence, increasing the self-consumption of renewable energy through demand response in households, local communities, and micro-grids is essential and calls for high demand prediction performance at lower levels of demand aggregations to achieve optimal performance. Although many of the recent studies have investigated both macro and micro scale short-term load forecasting (STLF), a comprehensive investigation on the effects of electrical demand aggregation size on STLF is minimal, especially with large sample sizes, where it is essential for optimal sizing of residential micro-grids, demand response markets, and virtual power plants. Hence, this study comprehensively investigates STLF of five aggregation levels (3, 10, 30, 100, and 479) based on a dataset of 479 residential dwellings in Osaka, Japan, with a sample size of (159, 47, 15, 4, and 1) per level, respectively, and investigates the underlying challenges in lower aggregation forecasting. Five deep learning (DL) methods are utilized for STLF and fine-tuned with extensive methodological sensitivity analysis and a variation of early stopping, where a detailed comparative analysis is developed. The test results reveal that a MAPE of (2.47–3.31%) close to country levels can be achieved on the highest aggregation, and below 10% can be sustained at 30 aggregated dwellings. Furthermore, the deep neural network (DNN) achieved the highest performance, followed by the Bi-directional Gated recurrent unit with fully connected layers (Bi-GRU-FCL), which had close to 15% faster training time and 40% fewer learnable parameters.

Feasibility Investigation for Residential Battery Sizing Considering EV Charging Demand

Photovoltaic (PV) systems along with battery energy storage systems (BESS) are an increasing trend for residential users due to the increasing cost of energy and environmental factors. Future sustainable grids will also have electric vehicles (EVs) integrated into these residential microgrids. However, this large-scale deployment of EVs and PV systems could mean several problems in terms of power quality, hosting capacity and as well economic implications. This paper aims to provide input to more optimal design and management of domestic PV and BESS for residential users with EVs. In this work, a measurement-based data set from a low-voltage distribution network in a rural area has been used. Investigation sees different household and PV-EV penetration levels to propose the BESS capacity and use cases. An economic analysis has been performed to check the feasibility of the proposed systems. The payback period is found to be between 13 to 15 years of the proposed systems.

Controlled V2Gs and battery integration into residential microgrids: Economic and environmental impacts

The increasing number of electric vehicles (EVs) represents a huge burden on the electrical grid. EVs’ charging and discharging control through vehicle-to-grid (V2G) techniques is one of the best solutions to power problems and CO2 emissions. This study introduces a multi-objective power scheduling of a residential microgrid that consists of PV, wind generator (WG), EVs, and battery energy storage system (BESS). It also presents a comparison between two energy management techniques: renewable base control (RBC) and load base control (LBC) for controlling plug-in-electric vehicles’ charging and discharging. The novelty of this work depends on holding a comparison between four sizing scenarios of residential load to verify the economic feasibility and environmental viability of integrating EVs into microgrids; and introducing a control technique for charging and discharging battery energy storage system (BESS) with the consideration of ToU prices and EVs’ power. Also, conducting a comparison between two optimization algorithms for confirming the effectiveness of the control techniques. The obtained results verify the effect of EVs control in reducing the total system cost and CO2 emissions. Integrating controlled EVs saves $ 0.728 million and deducts 213.23 kg of CO2 emissions. The controlled BESS integration reduces the total system cost by $ 0.835 million and CO2 emissions by 360.68 kg. The semi-isolated microgrid had the lowest overall cost of $ 1.421 million.

Optimal Planning of Residential Microgrids Based on Multiple Demand Response Programs Using ABC Algorithm

The smart grid has revolutionized the conventional electricity grid with the proposition of demand-side management (DSM). A DSM program enables the user to schedule its energy consumption in compliance with any pricing signal. This scheduling helps the grid operator to reduce the peak load demand and jointly benefits the user to reduce its electricity costs. Despite that, while doing so, it jeopardizes the user’s comfort. In the present paper, the authors have investigated the impact of communal DSM programs on the consumption patterns of users, including single as well as multiple households. The objective is to simultaneously minimize the electricity costs and user discomfort to make a win-win situation for both the grid operator and the user. Therefore, a multi-objective optimization problem (MOOP) has been formed to simultaneously minimize the daily electricity cost, peak to average ratio (PAR) of load demand, user discomfort, environmental emission, and total net present cost (TNPC). In order to evaluate the best scheduling method, sizing scenarios for a residential microgrid in a Southern Pakistani metropolis surrounded by rural areas are presented in this paper. The originality of this article comes from a comparison of the techno-economic and environmental performance of several sizing options for a residential load powered by renewable energy. The artificial bee colony (ABC) algorithm has been selected to solve the MOOP. The DSM programs are based upon different pricing signals, including real-time electricity pricing (RTEP), critical peak pricing (CPP), time of use (TOU), and day-ahead pricing (DAP) pricing. The results of the proposed ABC algorithm are compared with GA and standard algorithms, and they reveal the effectiveness of the proposed method. When demand response is used, the suggested optimization technique shows that the SH spring with PV/WG/grid-connected microgrid is the most investable-reliable sizing option with a minimal TNPC of $1405.18 for DAP tariff with SH spring. Additionally, with a reduction in emissions of 6699 kg/yr, DAP tariff with SH spring shows that PV/WG/DG/grid-connected system has the greatest impact on the environment. For DAP tariff with SH spring, optimal sizes of PV, WG and converter are 26.7 kW, 30 kW and 6.67 kW, respectively.

A Designed Energy Management System (EMS) for an Off- Grid Residential Microgrid

This paper proposes an Energy Management System (EMS) of an off-grid residential microgrid comprised of a solar photovoltaic array, wind turbine, and a battery-based energy storage system for a residential building in a remote area. The aim is to design a less complex energy management system that addresses energy resources dispatch challenge in a hybrid residential microgrid. It equitably dispatches resources and manages the generated energy to meet the residential load. The management control system algorithm is implemented using the state machine and simulated in MATLAB/Simulink environment for validation. Three scenarios are simulated, they emulate various contingencies scenarios that might rise due to the intermittency nature of the renewable resources. Simulation results show that the proposed energy management system achieves its design objectives. It maintains the power supply and demand balance, through an appropriate scheduling of resources even during contingency due to the intermittence nature of the renewable resources

Experimental Investigation of Inter-Phase Power Management in Residential Microgrids

With increased installation of single-phase rooftop PV systems, in-house battery storages, and high-power plug-in loads (i.e. EVs) at single-phase residential sites, it is prevalent that more and more distribution systems are becoming severely unbalanced causing power quality problems and thermal risks at distribution substations. To solve this problem, two main strategies have been developed previously by the authors, known as intra- and inter-phase power management strategies. The latter makes use of interconnecting back-to-back converters between the phases to mitigate the phase imbalance caused by the various single-phase DG units and loads. This paper devotes laboratory evaluation of the developed schemes and demonstrate key features experimentally on inter-phase power management. Two main experiments have been carried out to confirm that the developed technique can use the surplus power capacity from one phase to support the load demand in another phase to achieve dynamic power balance. From the point view of the substation transformer, the three-phases will always appear to be balanced despite the fact that different phases can have very different local generation and load profiles.

A decentralised power management strategy in a hybrid energy storage supported islanded residential sustainable DC microgrid system

A decentralised power management strategy in a hybrid energy storage supported islanded residential sustainable DC microgrid system

A decentralised power management strategy in a hybrid energy storage supported islanded residential sustainable DC microgrid system

A decentralised power management strategy in a hybrid energy storage supported islanded residential sustainable DC microgrid system

A Decentralized Game Theoretic Approach for Virtual Storage System Aggregation in a Residential Community

The current electricity market is better described as an oligopoly than a market of perfect competition from which, in fact, it may be rather far. The increasing penetration of residential distributed energy resources has led to a significant number of prosumers in the electricity market. Microgrid community, a group of single controllable entity prosumers, is a promising component of the smart grid which will potentially yield a free electricity market. In this paper, we present a novel formation for a residential community microgrid that includes a coalition of prosumer households with solar photovoltaic systems. These households are connected through a virtual power bank that consists of households’ storage batteries and that mediates the communications between the households and the main grid. Using an application of mean field game theory, we find Nash Equilibrium strategies under which such sharing could minimize a linear combination of the households’ energy generation cost, energy consumption cost and revenue of sold energy. The resulting approach is tested on a case study of a constructed community micro-grid in Montreal, Quebec, Canada. The proposed mean field game approach can help decrease the aggregated cost and the individual energy cost. A comparative analytical study on the benefit of sharing was also performed, demonstrating that each prosumer is expected to have at least a 40 percent reduction on their individual cost if belonging to a microgrid community of 100 prosumers located in Montreal city.

Grid Integration of DC Buildings: Standards, Requirements and Power Converter Topologies

Residential dc microgrids and nanogrids are the emerging technology that is aimed to promote the transition to energy-efficient buildings and provide simple, highly flexible integration of renewables, storages, and loads. At the same time, the mass acceptance of dc buildings is slowed down by the relative immaturity of the dc technology, lack of standardization and general awareness about its potential. Additional efforts from multiple directions are necessary to promote this technology and increase its market attractiveness. In the near-term, it is highly likely that the dc buildings will be connected to the conventional ac distribution grid by a front-end ac-dc converter that provides all the necessary protection and desired functionality. At the same time, the corresponding requirements for this converter have not been yet consolidated. To address this, present paper focuses on various aspects of the integration of dc buildings and includes analysis of related standards, directives, operational and compatibility requirements as well as classification of voltage levels. In addition, power converter configurations and modulation methods are analyzed and compared. A classification of topologies that can provide the required functionality for the application is proposed. Finally, future trends and remaining challenges pointed out to motivate new contributions to this topic.

Cloud Energy Storage Based Embedded Battery Technology Architecture for Residential Users Cost Minimization

This paper presents a cloud energy storage (CES) architecture for reducing energy costs for residential microgrid users. The former of this article concentrates on identifying an appropriate battery technology from various battery technologies with the aid of a simulation study. The later part addresses the economic feasibility of the storage architecture with three different scenarios namely grid connected energy storage, distributed energy storage (DES) and CES. The performance of the proposed architecture has been evaluated by considering five residential users with suitable battery technology identified from the former part of the study. For the purpose of the analysis, PV and load profiles including seasonal effects and grid price were taken from IIT Mumbai, India and IEX portal, respectively. In addition, this article also examines the impact of increased number of users with CES. The value of this study is that the proposed CES architecture is capable of reducing the cost of electricity experienced by the user by 11.37% as compared to DES. With this, CES operator’s revenue can be increased by 6.70% in summer and 16.97% in winter in the case of fixed number of users. Finally, based on the analysis and simulation results, this paper recommends CES with Li-ion battery technology for residential application.

Mathematical Foundations for Balancing Single-Phase Residential Microgrids Connected to a Three-Phase Distribution System

With the increased installation of single-phase rooftop PV systems, in-house battery storage units, and high-power plug-in loads (i.e., EVs) at single-phase residential sites, it is prevalent that an increasing number of residential distribution systems are becoming severely unbalanced, causing power quality problems and thermal risks at distribution sub-stations. While different techniques have been investigated to resolve this issue, there is still a lack of adequate theoretical foundations to guide these approaches. In this study, a detailed analytical analysis is carried out for a typical North American residential community with single-phase power generation, storage, and high-power random plug-in loads. This analysis has laid the foundation for a class of operating scenarios and provides an essential theoretical basis to unify different techniques for dynamically balancing single-phase microgrids connected to three-phase distribution systems. Detailed formulations have been developed for the first time to draw explicit power transfer relationships among power surplus and power-deficient phases to achieve an overall dynamic balance. A user-friendly, free interactive online tool has also been developed for potential users to evaluate their own application scenarios.

A new method for energy management of residential microgrid for sizing electrical and thermal storage systems

This paper proposes a frequency-based energy management scheme (FEMS) for islanded operation of a residential Microgrid (MG) using a Lithium-Ion battery (LIB) energy storage system (LIBESS) and distributed generators (DGs). The LIBESS handles primary frequency control and energy management during peak-load period while the dispatchable DGs supply the base load. Considering that the thermostatically controlled loads (TCLs) make up a major part of MG load profile especially during peak-load period in summer. Unlike TCLs like ACs and EHPs that instantaneously use their generated cooling thermal energy, the Thermal Energy storages (TESs) can also store it and use it later. In this paper the Thermal Energy Storages (TESs) are used instead of TCLs like ACs and EHPs to supply the cooling thermal energy for maintaining the indoor temperatures of residences within comfort limits. The TESs can be charged to provide cooling thermal energy during in islanded operation with insufficient power generation capacity. By doing this, the total energy consumption of MG and the required LIB capacity for energy management decreases considerably. The results shows that with TCLs being totally replaced with TESs, the required LIB capacity is decreased 70 % than the case TCLs are not replaced with TESs.

An Assessment of Electric Vehicles and Vehicle to Grid Operations for Residential Microgrids

An Assessment of Electric Vehicles and Vehicle to Grid Operations for Residential Microgrids

Microgrids Operation by Considering Demand Response and Supply Programs in the Presence of IGDT-Based Reverse Risk

Concerning the advantages of smart microgrids and the importance of selecting and using technologies accustomed to optimized planning and design of typology and capacity of supplies, demand response programs, and energy-storage charges, the existing research has focused on the optimized design of microgrids using ant colony optimization algorithm. Conditions of the optimization problem are enacted on the objective function based on technical and operational limitations of supplies and microgrids, which may lead to the limitation of response space of the problem. Additionally, a methodology is proposed for modeling and analyzing a novel design to consider the uncertainty of production and demand with reverse risk in the design of residential microgrids. The proposed methodology focuses on the uncertainty of photovoltaic production and load demand by solving two-dimensional multipurpose optimization problems based on information gap decision theory (IGDT). In the mentioned approach, the photovoltaic generation’s uncertainty and charge are integrated into an equation to be solved as a problem. Regardless of the likelihood density function of uncertainty parameters and without preparing a firm framework, the current method integrates wind and photovoltaic production into the microgrids. The results of the mentioned method are conclusive, which makes the problems solvable.

Microgrid Energy Management System for Residential Microgrid Using an Ensemble Forecasting Strategy and Grey Wolf Optimization

Microgrid (MG) is a small-scale grid that consists of multiple distributed energy resources and load demand. The microgrid energy management system (M-EMS) is the decision-making centre of the MG. An M-EMS is composed of four modules which are known as forecasting, scheduling, data acquisition, and human-machine interface. However, the forecasting and scheduling modules are considered the major modules from among the four of them. Therefore, this paper proposed an advanced microgrid energy management system (M-EMS) for grid-connected residential microgrid (MG) based on an ensemble forecasting strategy and grey wolf optimization (GWO) based scheduling strategy. In the forecasting module of M-EMS, the ensemble forecasting strategy is proposed to perform the short-term forecasting of PV power and load demand. The GWO based scheduling strategy has been proposed in scheduling module of M-EMS to minimize the operating cost of grid-connected residential MG. A small-scale experiment is conducted using Raspberry Pi 3 B+ via the python programming language to validate the effectiveness of the proposed M-EMS and real-time historical data of PV power, load demand, and weather is adopted as inputs. The performance of the proposed forecasting strategy is compared with ensemble forecasting strategy-1, particle swarm optimization based artificial neural network, and back-propagation neural network. The experimental results highlight that the proposed forecasting strategy outperforms the other strategies and achieved the lowest average value of normalized root mean square error of day-ahead prediction of PV power and load demand for the chosen day. Similarly, the performance of GWO based scheduling strategy of M-EMS is analyzed and compared for three different scenarios. Finally, the experimental results prove the outstanding performance of the proposed scheduling strategy.

Ultrawide Voltage Gain Range Microconverter for Integration of Silicon and Thin-Film Photovoltaic Modules in DC Microgrids

The article overviews residential and building-integrated photovoltaic (PV) module types. It demonstrates that existing dc-dc converter technologies do not allow building a universal interface microconverter because of wide dispersion of the parameters. However, the absence of universal microconverters limits the widespread adoption of PV modules despite great benefits for energy-efficient buildings and restrains the deployment of residential PV-based dc microgrids. Based on the design requirements formulated, a new approach utilizing a boost half-bridge front-end inverter and a three-mode reconfigurable rectifier (RR) is proposed. The article describes the operating principle of the derived converter. The RR allows keeping the duty cycle of the input side within a favorable range of acceptable efficiencies. An experimental prototype rated for 360 W was assembled and tested in the entire operating range. The experimental results prove the ultrawide input voltage range of over 1:20 and demonstrate the capability of the proposed concept to interface different PV module types while enabling their shade-tolerant operation.