Decentralized Multi-Agent System Applied to the Decision Making Process of the Microgrid Restoration Procedure towards Sustainability

A significant procedure to ensure the consumer supply is Power System Restoration (PSR). Due to the increase of the number of distributed generators in the grid, it is possible to shift from the conventional PSR to a new strategy involving the use of distributed energy resources (DER). In this paper, a decentralized multi-agent system (MAS) is proposed to cope with the restoration procedure in a microgrid (MG). Each agent is assigned to a specific consumer or microsource (MS), communicating with other agents at every stage of the restoration procedure so that a common decision is reached. The 0/1 knapsack problem is the problem that every agent solves to determine the best load connection sequence during the restoration of the MG. Two different case studies are used to test the MAS on a dynamically modeled benchmark MG: a total blackout and a partial blackout. Regarding the partial blackout case, demand response emergency programs are considered to manage the loads in the MG. The MAS is developed in Matlab/Simulink environment and by performing the corresponding dynamic simulations it is possible to validate this system towards sustainability

Microgrids and Resilience to Climate-Driven Impacts on Public Health

“Resilience” has burst into the lexicons of several policy areas in recent years, owing in no small part to climate change’s amplification of extreme events that severely disrupt the operation of natural, social, and engineered systems. Fostering resilience means anticipating severe disruptions and planning, investing, and designing so that such disruptions, which are certain to occur, are made shallower in depth and shorter in duration. Thus a resilient system or community can continue functioning despite disruptive events, return more swiftly to routine function following disruption, and incorporate new information so as to improve operations in extremis and speed future restorations. As different policy communities apply the concept of resilience to their respective missions, they emphasize different objectives. This article examines how the definitions adopted by the public health and electricity communities can, but do not necessarily, converge in responses to electricity outages so severe that they affect the operation of critical infrastructure, such as wastewater treatment and drinking water facilities, hospitals, and cooling centers. Currently, such outages cause a form of handoff from utilities to their customers: grid power fails and a small constellation of backup generators maintained by atomized campuses, facilities, or individual structures switch on, or fail to switch on, or were never purchased and so leave the location dark and its equipment inoperative. This handoff is operational, but it reflects legal obligations—and their limits. Enter the microgrid, a specially designed segment of the electricity distribution grid’s mesh that can either operate seamlessly as part of the wider grid, or as an independent “island” that serves some or all of the electricity users within its boundary even when the wider grid fails. Microgrids can, but do not necessarily, mitigate the adverse public health implications of the handoff that accompanies widespread and severe grid failure. To encourage the convergence of public health and electricity policy priorities in decisions about microgrid siting, design, and operation, this article makes several recommendations. Some of these should ideally be taken up at the federal level, but the bulk of the work they recommend should take place at the state-level, and would necessarily be implemented at the state and local levels

Microgrids and Resilience to Climate-Driven Impacts on Public Health

“Resilience” has burst into the lexicons of several policy areas in recent years, owing in no small part to climate change’s amplification of extreme events that severely disrupt the operation of natural, social, and engineered systems. Fostering resilience means anticipating severe disruptions and planning, investing, and designing so that such disruptions, which are certain to occur, are made shallower in depth and shorter in duration. Thus a resilient system or community can continue functioning despite disruptive events, return more swiftly to routine function following disruption, and incorporate new information so as to improve operations in extremis and speed future restorations. As different policy communities apply the concept of resilience to their respective missions, they emphasize different objectives. This article examines how the definitions adopted by the public health and electricity communities can, but do not necessarily, converge in responses to electricity outages so severe that they affect the operation of critical infrastructure, such as wastewater treatment and drinking water facilities, hospitals, and cooling centers. Currently, such outages cause a form of handoff from utilities to their customers: grid power fails and a small constellation of backup generators maintained by atomized campuses, facilities, or individual structures switch on, or fail to switch on, or were never purchased and so leave the location dark and its equipment inoperative. This handoff is operational, but it reflects legal obligations – and their limits. Enter the microgrid, a specially designed segment of the electricity distribution grid’s mesh that can either operate seamlessly as part of the wider grid, or as an independent “island” that serves some or all of the electricity users within its boundary even when the wider grid fails. Microgrids can, but do not necessarily, mitigate the adverse public health implications of the handoff that accompanies widespread and severe grid failure. To encourage the convergence of public health and electricity policy priorities in decisions about microgrid siting, design, and operation, this article makes several recommendations. Some of these should ideally be taken up at the federal level, but the bulk of the work they recommend should take place at the state-level, and would necessarily be implemented at the state and local levels

Using Renewable-Based Microgrid Capabilities for Power System Restoration

Power system restoration (PSR) is a very important procedure to ensure the consumer supply. In this paper, a decentralized multi-agent system (MAS) for dealing with the microgrid restoration procedure is proposed. In this proposed method, each agent is associated to a consumer or microsource (MS) and these will communicate between each other in order to reach a common decision. The agents solve a 0/1 knapsack problem to determine the best load connection sequence during the microgrid restoration procedure. The proposed MAS is tested in two different case studies: a total blackout and a partial blackout, in which the emergency demand response programs are considered. It is developed in the Matlab/Simulink environment and is validated by performing the corresponding dynamic simulations.Power system restoration (PSR) is a very important procedure to ensure the consumer supply. In this paper, a decentralized multi-agent system (MAS) for dealing with the microgrid restoration procedure is proposed. In this proposed method, each agent is associated to a consumer or microsource (MS) and these will communicate between each other in order to reach a common decision. The agents solve a 0/1 knapsack problem to determine the best load connection sequence during the microgrid restoration procedure. The proposed MAS is tested in two different case studies: a total blackout and a partial blackout, in which the emergency demand response programs are considered. It is developed in the Matlab/Simulink environment and is validated by performing the corresponding dynamic simulations

Optimalus galios paskirstymas mikrotinkle

Internationally, power demand is booming so, renewable energy based distributed generator and Microgrid spider network will increasingly play a decisive role in electricity production, distribution and most advantageous level power sharing. A Microgrid comprises of various sort of load and disseminated generators that work as a solitary controllable framework. Presently a day, control gadgets are on edge about wellbeing when the utility network associated with Microgrid or other inexhaustible assets at interconnected or islanded mode. The main goal of this thesis is to inspect and propose using a multi-input DC-DC power converter for grid connected hybrid Microgrid system to reduce the cost and share the power at optimal level. In the system, it consists of the multi-input DC-DC converter and full-bridge DC-AC IGBT inverter. If the fluctuation is more in the output of renewable energy resources than maximum power point tracking algorithm is used by input sources of PV and the wind. The integration of DC bus and Hybrid Microgrid renewable power supply system implemented and simulated using MATLAB/SIMULINK as compared to use directly AC bus. Methods that have been used to control stability in Microgrid by load sharing are analysed in this thesis. Also in this thesis, a price-based demand response is proposed using to share power in Microgrid. By utilizing this technique it is less demanding to keep up security amongst request and era. It is an imperative about dynamic and receptive power control in the power framework, particularly when the diverse sort of Microgrid sustainable power source assets associated with the utility network. As a result, how the modelling of the power grid infrastructure with Microgrid connected renewable energy resources are controlled and discussed here and the result of controlling load, frequency, voltage and current are achieved here. On the basis of parameters results the power sharing is possible at optimal level with system balancing and reduce the cost

A resilience-oriented bidirectional anfis framework for networked microgrid management

This study implemented a bidirectional artificial neuro-fuzzy inference system (ANFIS) to solve the problem of system resilience in synchronized and islanded grid mode/operation (during normal operation and in the event of a catastrophic disaster, respectively). Included in this setup are photovoltaics, wind turbines, batteries, and smart load management. Solar panels, wind turbines, and battery-charging supercapacitors are just a few of the sustainable energy sources ANFIS coordinates. The first step in the process was the development of a mode-specific control algorithm to address the system’s current behavior. Relative ANFIS will take over to greatly boost resilience during times of crisis, power savings, and routine operations. A bidirectional converter connects the battery in order to keep the DC link stable and allow energy displacement due to changes in generation and consumption. When combined with the ANFIS algorithm, PV can be used to meet precise power needs. This means it can safeguard the battery from extreme conditions such as overcharging or discharging. The wind system is optimized for an island environment and will perform as designed. The efficiency of the system and the life of the batteries both improve. Improvements to the inverter’s functionality can be attributed to the use of synchronous reference frame transformation for control. Based on the available solar power, wind power, and system state of charge (SOC), the anticipated fuzzy rule-based ANFIS will take over. Furthermore, the synchronized grid was compared to ANFIS. The study uses MATLAB/Simulink to demonstrate the robustness of the system under test

Distributed Power Generation in Europe: Technical Issues for Further Integration

The electric power sector in Europe is currently facing different changes and evolutions mainly in response to the three issues at EU level - environmental sustainability, security of supply, and competitiveness. These issues, against a background of growing electricity demand, may represent drivers for facilitating the further deployment of Distributed Power Generation technologies in Europe. The present Report focuses on the potential role of Distributed Power Generation (or simply Distributed Generation, DG) in a European perspective. More specifically, this work aims to assess the technical issues and developments related to DG technologies and their integration into the European power systems. As a starting point the concept of Distributed Generation is characterised for the purpose of the study. Distributed Generation, defined as an electric power source connected to the distribution network, serving a customer on-site or providing network support, may offer various benefits to the European electric power systems. DG technologies may consist of small/medium size, modular energy conversion units, which are generally located close to end users and transform primary energy resources into electricity and eventually heat. There are, however, major issues concerning the integration of DG technology into the distribution networks. In fact, the existing distribution networks were not generally designed to operate in presence of DG technologies. Consequently, a sustained increase in the deployment of DG resources may imply several changes in the electric power system architecture in the near future. The present Report on Distributed Generation in Europe, after an overview of the basic elements of electric power systems, introduces the proposed definition and main features of DG. Then, it reviews the state-of-the-art of DG technologies as well as focuses on current DG grid integration issues. Technical solutions towards DG integration in Europe and developments concerning the future distribution systems are also addressed in the study.JRC.F.7-Energy systems evaluatio

Energy Management of Grid-Connected Microgrids, Incorporating Battery Energy Storage and CHP Systems Using Mixed Integer Linear Programming

In this thesis, an energy management system (EMS) is proposed for use with battery energy storage systems (BESS) in solar photovoltaic-based (PV-BESS) grid-connected microgrids and combined heat and power (CHP) applications. As a result, the battery's charge/discharge power is optimised so that the overall cost of energy consumed is minimised, considering the variation in grid tariff, renewable power generation and load demand. The system is modelled as an economic load dispatch optimisation problem over a 24-hour time horizon and solved using mixed integer linear programming (MILP) for the grid-connected Microgrid and the CHP application. However, this formulation requires information about the predicted renewable energy power generation and load demand over the next 24 hours. Therefore, a long short-term memory (LSTM) neural network is proposed to achieve this. The receding horizon (RH) strategy is suggested to reduce the impact of prediction error and enable real-time implementation of the energy management system (EMS) that benefits from using actual generation and demand data in real-time. At each time-step, the LSTM predicts the generation and load data for the next 24 h. The dispatch problem is then solved, and the real-time battery charging or discharging command for only the first hour is applied. Real data are then used to update the LSTM input, and the process is repeated. Simulation results using the Ushant Island as a case study show that the proposed online optimisation strategy outperforms the offline optimisation strategy (with no RH), reducing the operating cost by 6.12%. The analyses of the impact of different times of use (TOU) and standard tariff in the energy management of grid-connected microgrids as it relates to the charge/discharge cycle of the BESS and the optimal operating cost of the Microgrid using the LSTM-MILP-RH approach is evaluated. Four tariffs UK tariff schemes are considered: (1) Residential TOU tariff (RTOU), (2) Economy seven tariff (E7T), (3) Economy ten tariff (E10T), and (4) Standard tariff (STD). It was found that the RTOU tariff scheme gives the lowest operating cost, followed by the E10T tariff scheme with savings of 63.5% and 55.5%, respectively, compared to the grid-only operation. However, the RTOU and E10 tariff scheme is mainly used for residential applications with the duck curve load demand structure. For community grid-connected microgrid applications except for residential-only communities, the E7T and STD, with 54.2% and 39.9%, respectively, are the most likely options offered by energy suppliers. The use of combined heat and power (CHP) systems has recently increased due to their high combined efficiency and low emissions. Using CHP systems in behind-the-meter applications, however, can introduce some challenges. Firstly, the CHP system must operate in load-following mode to prevent power export to the grid. Secondly, if the load drops below a predefined threshold, the engine will operate at a lower temperature and hence lower efficiency, as the fuel is only half-burnt, creating significant emissions. The aforementioned issues may be solved by combining CHP with a battery energy storage system. However, the dispatch of CHP and BESS must be optimised. Offline optimisation methods based on load prediction will not prevent power export to the grid due to prediction errors. Therefore, a real-time EMS using a combination of LSTM neural networks, MILP, and RH control strategy is proposed. Simulation results show that the proposed method can prevent power export to the grid and reduce the operational cost by 8.75% compared to the offline method. The finding shows that the BESS is a valuable asset for sustainable energy transition. However, they must be operated safely to guarantee operational cost reduction and longer life for the BESS

The implementation of energy sharing using a system of systems approach

There is an increasing demand for renewable energy and consumers need more procurement options to meet their needs. Energy sharing provides a peer-to-peer (P2P) marketplace where prosumer electricity is redistributed to fellow energy-sharing community participants. This redistribution of prosumer electricity provides consumers with additional electricity suppliers, while also decreasing the load on the utility company. Though significant progress has been made regarding research and implementation of energy sharing, there is still room for growth when evaluating energy-sharing communities and defining appropriate community coordination based on end-user needs. The first contribution in this work identified nine characteristics of energy-sharing communities as a decentralized complex adaptive system of systems (DCASoS). Considering each characteristic before determining community coordination is vital to ensure ample participation within the energy-sharing community. The second contribution was the exploration of a two-stage stochastic programming model as an alternative to the classic energy distribution business model. The third contribution compares three behavioral theories to identify the best fitting model to predict interest in participating in an energysharing community. This research provides companies with foundational knowledge to develop an energy-sharing community that both fulfills end-user satisfaction and increases robustness of electricity distribution business models --Abstract, page iv