2,664 research outputs found

    Forecast-based Energy Management Systems

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    The high integration of distributed energy resources into the domestic level has led to an increase in the number of consumers becoming prosumers (producer + customer), which creates several challenges for network operators, such as controlling renewable energy sources over-generation. Recently, self-consumption as a new approach is encouraged by several countries to reduce the dependency on the national grid. This work presents two different Energy Management System (EMS) algorithms for a domestic Photovoltaic (PV) system: (a) real-time Fuzzy Logic-based EMS (FL-EMS) and (b) day-ahead Mixed Integer Linear Programming-based EMS (MILP-EMS). Both methods are tested using the data from the Active Office Building (AOB) located in Swansea University, Bay Campus, UK, as a case study to demonstrate the developed EMSs. AOB comprises a PV system and a Li-ion Battery Storage System (BSS) connected to the grid. The MILP-EMS is used to develop a Community Energy Management System (CEMS) to facilitate local energy exchange. CEMS is tested using the data from six houses located in London, UK, to form a community. Each household comprises a PV system and BSS connected to the grid. It is assumed that all six households use an EV and are equipped with a bidirectional charger to facilitate the Vehicle to House (V2H) mode. In addition, two shiftable appliances are considered to shift the demand to the times when PV generation is maximum to maximise community local consumption. MATLAB software is used to code the proposed systems. The FL-EMS exploits day-ahead energy forecast (assumed it is available from a third party) to control the BSS with the aim of reducing the net energy exchange with the grid by enhancing PV self-consumption. The FL-EMS determines the optimal settings for the BSS, taking into consideration the BSS's state of health to maximise its lifetime. The results are compared with recently published works to demonstrate the effectiveness of the proposed method. The proposed FL-EMS saves 18% on total energy costs in six months compared to a similar system that utilises a day-ahead energy forecast. In addition, the method shows a considerable reduction in the net energy exchanged between the AOB and the grid. The main objective of the MILP-EMS is to reduce the net energy exchange with the grid by including a two days-ahead energy forecast in the optimisation process. The proposed method reduces the total operating costs (energy cost + BSS degradation cost) by up to 35% over six months and reduces net energy exchanged with the grid compared to similar energy optimisation technique. The proposed cost function in MILP-EMS shows that it can outperform the performance of alternative cost function that directly reduce the net energy exchange. CEMS uses two days-ahead energy forecast to reduce the net energy exchange with the grid by coordinating the distributed BSSs. The proposed CEMS reduces the total operating costs (energy costs + BSSs degradation costs) of the community by 7.6% when compared to the six houses being operated individually. In addition, the proposed CEMS enhances community self-consumption by reducing the net energy exchange with the grid by 25.3% over four months compared to similar community energy optimisation technique. A further reduction in operating costs is achieved using V2H mode and including shiftable appliances. Results show that introducing the V2H mode reduces both the total operating costs of the community and the net energy exchange with the grid

    Smart Energy Management for Smart Grids

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    This book is a contribution from the authors, to share solutions for a better and sustainable power grid. Renewable energy, smart grid security and smart energy management are the main topics discussed in this book

    Domestic load management with coordinated photovoltaics, battery storage and electric vehicle operation

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    Coordinated power demand management at residential or domestic levels allows energy participants to efficiently manage load profiles, increase energy efficiency and reduce operational cost. In this paper, a hierarchical coordination framework to optimally manage domestic load using photovoltaic (PV) units, battery-energy-storage-systems (BESs) and electric vehicles (EVs) is presented. The bidirectional power flow of EV with vehicle to grid (V2G) operation manages real-time domestic load profile and takes appropriate coordinated action using its controller when necessary. The proposed system has been applied to a real power distribution network and tested with real load patterns and load dynamics. This also includes various test scenarios and prosumer's preferences e.g., with or without EVs, number of EV owners, number of households, and prosumer's daily activities. This is a combined hybrid system for hierarchical coordination that consists of PV units, BES systems and EVs. The system performance was analyzed with different commercial EV types with charging/ discharging constraints and the result shows that the domestic load demand on the distribution grid during the peak period has been reduced significantly. In the end, this proposed system's performance was compared with the prediction-based test techniques and the financial benefits were estimated. © 2013 IEEE

    A review of community electrical energy systems

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    This paper is aimed at new entrants into the field of electrical community energy. It reviews some of the work that is underway into community electrical energy projects. This review includes a summary of key issues and components which need consideration including some or all of the following; demand side management, energy storage (including vehicle to grid) and renewable generation. The paper looks further into the energy management schemes of these projects and summarises previously published methodology in the area

    Forecast-Based Energy Management for Domestic PV-Battery Systems: A U.K. Case Study

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    This paper presents a predictive Energy Management System (EMS), aimed to improve the per-formance of a domestic PV-battery system and maximize self-consumption by minimizing energy exchange with the utility grid. The proposed algorithm facilitates a self-consumption approach, which reduces electricity bills, transmission losses, and the required central generation/storage systems. The proposed EMS uses a com-bination of Fuzzy Logic (FL) and a rule based-algorithm to optimally control the PV-battery system while con-sidering the day-ahead energy forecast including forecast error and the battery State of Health (SOH). The FL maximizes the lifetime of the battery by using SOH and State of Charge (SOC) in decision making algorithm to charge/discharge the battery. The proposed Battery Management System (BMS) has been tested using Active Office Building (AOB) located in Swansea University, UK. Furthermore, it is compared with three recently published methods and with the current BMS utilized in the AOB to show the effectiveness of the proposed technique. The results show that the proposed BMS achieves a saving of 18% in the total energy cost over six months compared to a similar day-ahead forecast-based work. It also achieves a saving up to 95% compared to other methods (with a similar structure) but without a day-ahead forecast-based management. The proposed BMS enhances the battery's lifetime by reducing the average SOC up to 47% compared to the previous methods through avoiding unnecessary charge and discharge cycles. The impact of the PV system size and the battery capacity on the net exchanged energy with the utility grid is also investigated in this study

    Real-Time Energy Management for a Small Scale PV-Battery Microgrid: Modeling, Design, and Experimental Verification

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    A new energy management system (EMS) is presented for small scale microgrids (MGs). The proposed EMS focuses on minimizing the daily cost of the energy drawn by the MG from the main electrical grid and increasing the self-consumption of local renewable energy resources (RES). This is achieved by determining the appropriate reference value for the power drawn from the main grid and forcing the MG to accurately follow this value by controlling a battery energy storage system. A mixed integer linear programming algorithm determines this reference value considering a time-of-use tariff and short-term forecasting of generation and consumption. A real-time predictive controller is used to control the battery energy storage system to follow this reference value. The results obtained show the capability of the proposed EMS to lower the daily operating costs for the MG customers. Experimental studies on a laboratory-based MG have been implemented to demonstrate that the proposed EMS can be implemented in a realistic environment

    A Smart Battery Management System for Photovoltaic Plants in Households Based on Raw Production Forecast

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    A basic battery management system (BMS) permits the safe charge/discharge of the batteries and the supply of loads. Batteries are protected to avoid fast degradation: the minimum and maximum state-of-charge (SOC) limits are not exceeded and fast charge/discharge cycles are not permitted. A more sophisticated BMS connected to a photovoltaic (PV) generator could also work with the double purpose of protecting storage and reducing peak demand. Peak reduction by storage generally requires the forecast of consumption and PV generation profiles to perform a provisional energy balance. To do it, it is required to have accurate information about production profiles, that is, to have at disposal accurate weather forecasts, which are not easily available. In the present work, an efficient BMS in grid-connected PV plants for residential users is described. Starting from raw 1-day ahead weather forecast and prediction of consumption, the proposed BMS preserves battery charge when it is expected high load and low PV production and performs peak shaving with a negligible reduction in self-sufficiency

    DEVELOPMENT OF FORECASTING AND SCHEDULING METHODS AND DATA ANALYTICS BASED CONTROLS FOR SMART LOADS IN RESIDENTIAL BUILDINGS.

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    DEVELOPMENT OF FORECASTING AND SCHEDULING METHODS AND DATA ANALYTICS BASED CONTROLS FOR SMART LOADS IN RESIDENTIAL BUILDINGS

    Smart home energy management: An analysis of a novel dynamic pricing and demand response aware control algorithm for households with distributed renewable energy generation and storage

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    Home energy management systems (HEMS) technology can provide a smart and efficient way of optimising energy usage in residential buildings. One of the main goals of the Smart Grid is to achieve Demand Response (DR) by increasing end users’ participation in decision making and increasing the level of awareness that will lead them to manage their energy consumption in an efficient way. This research presents an intelligent HEMS algorithm that manages and controls a range of household appliances with different demand response (DR) limits in an automated way without requiring consumer intervention. In addition, a novel Multiple Users and Load Priority (MULP) scheme is proposed to organise and schedule the list of load priorities in advance for multiple users sharing a house and its appliances. This algorithm focuses on control strategies for controllable loads including air-conditioners, dishwashers, clothes dryers, water heaters, pool pumps and electrical vehicles. Moreover, to investigate the impact on efficiency and reliability of the proposed HEMS algorithm, small-scale renewable energy generation facilities and energy storage systems (ESSs), including batteries and electric vehicles have been incorporated. To achieve this goal, different mathematical optimisation approaches such as linear programming, heuristic methods and genetic algorithms have been applied for optimising the schedule of residential loads using different demand side management and demand response programs as well as optimising the size of a grid connected renewable energy system. Thorough incorporation of a single objective optimisation problem under different system constraints, the proposed algorithm not only reduces the residential energy usage and utility bills, but also determines an optimal scheduling for appliances to minimise any impacts on the level of consumer comfort. To verify the efficiency and robustness of the proposed algorithm a number of simulations were performed under different scenarios. The simulations for load scheduling were carried out over 24 hour periods based on real-time and day ahead electricity prices. The results obtained showed that the proposed MULP scheme resulted in a noticeable decrease in the electricity bill when compared to the other scenarios with no automated scheduling and when a renewable energy system and ESS are not incorporated. Additionally, further simulation results showed that widespread deployment of small scale fixed energy storage and electric vehicle battery storage alongside an intelligent HEMS could enable additional reductions in peak energy usage, and household energy cost. Furthermore, the results also showed that incorporating an optimally designed grid-connected renewable energy system into the proposed HEMS algorithm could significantly reduce household electricity bills, maintain comfort levels, and reduce the environmental footprint. The results of this research are considered to be of great significance as the proposed HEMS approach may help reduce the cost of integrating renewable energy resources into the national grid, which will be reflected in more users adopting these technologies. This in turn will lead to a reduction in the dependence on traditional energy resources that can have negative impacts on the environment. In particular, if a significant proportion of households in a region were to implement the proposed HEMS with the incorporation of small scale storage, then the overall peak demand could be significantly reduced providing great benefits to the grid operator as well as the households
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