scholarly journals An Optimized Framework for Energy Management of Multi-Microgrid Systems

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6012
Author(s):  
Komal Naz ◽  
Fasiha Zainab ◽  
Khawaja Khalid Mehmood ◽  
Syed Basit Ali Bukhari ◽  
Hassan Abdullah Khalid ◽  
...  
Keyword(s):  
Energy Management ◽  
Distribution System ◽  
Management System ◽  
Storage System ◽  
Energy Storage System ◽  
Green Energy ◽  
Energy Management System ◽  
Load Curtailment ◽  
Battery Energy ◽  
Available Resources

Regarding different challenges, such as integration of green energy and autonomy of microgrid (MG) in the multi-microgrid (MMG) system, this paper presents an optimized and coordinated strategy for energy management of MMG systems that consider multiple scenarios of MGs. The proposed strategy operates at two optimization levels: local and global. At an MG level, each energy management system satisfies its local demand by utilizing all available resources via local optimization, and only sends surplus/deficit energy data signals to MMG level, which enhances customer privacy. Thereafter, at an MMG level, a central energy management system performs global optimization and selects optimized options from the available resources, which include charging/discharging energy to/from the community battery energy storage system, selling/buying power to/from other MGs, and trading with the grid. Two types of loads are considered in this model: sensitive and non-sensitive. The algorithm tries to make the system reliable by avoiding utmost load curtailment and prefers to shed non-sensitive loads over sensitive loads in the case of load shedding. To verify the robustness of the proposed scheme, several test cases are generated by Monte Carlo Simulations and simulated on the IEEE 33-bus distribution system. The results show the effectiveness of the proposed model.

scholarly journals A Stochastic MPC Based Energy Management System for Simultaneous Participation in Continuous and Discrete Prosumer-to-Prosumer Energy Markets

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3751
Author(s):  
Pablo Baez-Gonzalez ◽  
Felix Garcia-Torres ◽  
Miguel A. Ridao ◽  
Carlos Bordons
Keyword(s):  
Energy Management ◽  
Management System ◽  
Storage System ◽  
Energy Storage System ◽  
Energy Management System ◽  
Bidding Strategies ◽  
Production And Consumption ◽  
Optimal Bidding ◽  
Continuous Trading ◽  
Stochastic Model Predictive Control

This article studies the exchange of self-produced renewable energy between prosumers (and with pure end consumers), through the discrete trading of energy packages and proposes a framework for optimizing this exchange. In order to mitigate the imbalances derived from discrepancies between production and consumption and their respective forecasts, the simultaneous continuous trading of instantaneous power quotas is proposed, giving rise to a time-ahead market running in parallel with a real-time one. An energy management system (EMS) based on stochastic model predictive control (SMPC) simultaneously determines the optimal bidding strategies for both markets, as well as the optimal utilisation of any energy storage system (ESS). Simulations carried out for a heterogeneous group of agents show that those with SMPC-EMS achieve savings of between 3% and 15% in their energy operation economic result. The proposed structures allows the peer-to-peer (P2P) energy trading between end users without ESS and constitute a viable alternative to avoid deviation penalties in secondary regulation markets.

scholarly journals Multi-Objective Fuzzy Logic-Based Energy Management System for Microgrids with Battery and Hydrogen Energy Storage System

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1074 ◽  
Author(s):  
Francisco José Vivas ◽  
Francisca Segura ◽  
José Manuel Andújar ◽  
Adriana Palacio ◽  
Jaime Luis Saenz ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Energy Storage ◽  
Energy Management ◽  
Management System ◽  
Storage System ◽  
Energy Storage System ◽  
Energy Management System ◽  
Hydrogen Energy ◽  
Economic Point ◽  
Load Demand

This paper proposes a fuzzy logic-based energy management system (EMS) for microgrids with a combined battery and hydrogen energy storage system (ESS), which ensures the power balance according to the load demand at the time that it takes into account the improvement of the microgrid performance from a technical and economic point of view. As is known, renewable energy-based microgrids are receiving increasing interest in the research community, since they play a key role in the challenge of designing the next energy transition model. The integration of ESSs allows the absorption of the energy surplus in the microgrid to ensure power supply if the renewable resource is insufficient and the microgrid is isolated. If the microgrid can be connected to the main power grid, the freedom degrees increase and this allows, among other things, diminishment of the ESS size. Planning the operation of renewable sources-based microgrids requires both an efficient dispatching management between the available and the demanded energy and a reliable forecasting tool. The developed EMS is based on a fuzzy logic controller (FLC), which presents different advantages regarding other controllers: It is not necessary to know the model of the plant, and the linguistic rules that make up its inference engine are easily interpretable. These rules can incorporate expert knowledge, which simplifies the microgrid management, generally complex. The developed EMS has been subjected to a stress test that has demonstrated its excellent behavior. For that, a residential-type profile in an actual microgrid has been used. The developed fuzzy logic-based EMS, in addition to responding to the required load demand, can meet both technical (to prolong the devices’ lifespan) and economic (seeking the highest profitability and efficiency) established criteria, which can be introduced by the expert depending on the microgrid characteristic and profile demand to accomplish.

scholarly journals Reinforcement Learning Based Energy Management Algorithm for Smart Energy Buildings

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2010 ◽  
Author(s):  
Sunyong Kim ◽  
Hyuk Lim
Keyword(s):  
Reinforcement Learning ◽  
Smart Grid ◽  
Energy Management ◽  
Management System ◽  
Transition Probability ◽  
Storage System ◽  
Energy Storage System ◽  
Energy Costs ◽  
Energy Management System ◽  
Management Algorithm

A smart grid facilitates more effective energy management of an electrical grid system. Because both energy consumption and associated building operation costs are increasing rapidly around the world, the need for flexible and cost-effective management of the energy used by buildings in a smart grid environment is increasing. In this paper, we consider an energy management system for a smart energy building connected to an external grid (utility) as well as distributed energy resources including a renewable energy source, energy storage system, and vehicle-to-grid station. First, the energy management system is modeled using a Markov decision process that completely describes the state, action, transition probability, and rewards of the system. Subsequently, a reinforcement-learning-based energy management algorithm is proposed to reduce the operation energy costs of the target smart energy building under unknown future information. The results of numerical simulation based on the data measured in real environments show that the proposed energy management algorithm gradually reduces energy costs via learning processes compared to other random and non-learning-based algorithms.

Energy management system for stand-alone diesel-wind-biomass microgrid with energy storage system

Energy ◽  
2016 ◽  
Vol 97 ◽  
pp. 90-104 ◽  
Author(s):  
Chengshan Wang ◽  
Yixin Liu ◽  
Xialin Li ◽  
Li Guo ◽  
Lei Qiao ◽  
...  
Keyword(s):  
Energy Storage ◽  
Energy Management ◽  
Management System ◽  
Storage System ◽  
Energy Storage System ◽  
Energy Management System

scholarly journals Deep Reinforcement Learning for Energy Management in a Microgrid with Flexible Demand

2020 ◽  
Author(s):  
Taha abd el halim Nakabi ◽  
Pekka Toivanen
Keyword(s):  
Reinforcement Learning ◽  
Energy Management ◽  
Management System ◽  
Storage System ◽  
Learning Algorithms ◽  
Model Performance ◽  
Energy Storage System ◽  
Energy Management System ◽  
Optimal Policies ◽  
Experience Replay

In this paper, we study the performance of various deep reinforcement learning algorithms to enhance the energy management system of a microgrid. We propose a novel microgrid model that consists of a wind turbine generator, an energy storage system, a set of thermostatically controlled loads, a set of price-responsive loads, and a connection to the main grid. The proposed energy management system is designed to coordinate among the different flexible sources by defining the priority resources, direct demand control signals, and electricity prices. Seven deep reinforcement learning algorithms were implemented and are empirically compared in this paper. The numerical results show that the deep reinforcement learning algorithms differ widely in their ability to converge to optimal policies. By adding an experience replay and a semi-deterministic training phase to the well-known asynchronous advantage actor-critic algorithm, we achieved the highest model performance as well as convergence to near-optimal policies.

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