Robust Control of a Microgrid Energy Storage System Using Various Approaches

Choi, Jong-Woo; Shin, Youngmee; Choi, Moonok; Park, Wan-Ki; Lee, Il-Woo

doi:10.1109/tsg.2018.2808914

Cited by 39 publications

(33 citation statements)

References 26 publications

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“…Third, the simple approach cannot be extended directly to setups with multiple renewable generation units and battery storages. A more sophisticated approach to handle volatile forecasts while taking battery health explicitly into account should be considered in future research, e.g., robust control [46] or stochastic optimization [47].…”

Section: Short-term Updatementioning

confidence: 99%

Self-Learning Data-Based Models as Basis of a Universally Applicable Energy Management System

et al. 2020

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In the transfer from fossil fuels to renewable energies, grid operators, companies and farms develop an increasing interest in smart energy management systems which can reduce their energy expenses. This requires sufficiently detailed models of the underlying components and forecasts of generation and consumption over future time horizons. In this work, it is investigated via a real-world case study how data-based methods based on regression and clustering can be applied to this task, such that potentially extensive effort for physical modeling can be decreased. Models and automated update mechanisms are derived from measurement data for a photovoltaic plant, a heat pump, a battery storage, and a washing machine. A smart energy system is realized in a real household to exploit the resulting models for minimizing energy expenses via optimization of self-consumption. Experimental data are presented that illustrate the models' performance in the real-world system. The study concludes that it is possible to build a smart adaptive forecast-based energy management system without expert knowledge of detailed physics of system components, but special care must be taken in several aspects of system design to avoid undesired effects which decrease the overall system performance.Energies 2020, 13, 2084 2 of 42 to exploit these installations by increasing self-consumption, i.e., maximizing the amount of locally produced energy that is directly used.However, to perform such a task in an automated way, smart energy systems are needed which are able to determine and execute optimal operation strategies by rescheduling controllable loads and control of storage devices in accordance with the expected energy production profile. In simple system setups such as integrated PV-storage systems, relay-based self-consumption maximization strategies might suffice and are commonly found in practice [6]. For more complex system setups with several controllable devices, in many cases, more advanced optimization strategies are proposed, which require sufficiently detailed models of system components [7]. In the case of a large office building, a farm, or an industrial premise, loads and devices exhibiting very specific or complex consumption behaviors might prohibit the usage of advanced optimization methods due to the effort of deriving and implementing the necessary white or gray box models. This situation was reported by Žáčeková et al. [8] and Sturzenegger et al. [9] for the usage of model predictive control (MPC) in the minimization of the energy demand of office buildings. Furthermore, the application of forecast methods for the expected power generation is investigated in several studies, indicating financial benefits for a smart energy system or its integration into the overall power system (e.g., [6,10,11]). If these benefits are to be exploited in a smart energy system, forecast models for volatile generation and consumption must also be developed, increasing the initial modeling effort even further.In the literature, there are basi...

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Section: Short-term Updatementioning

confidence: 99%

Self-Learning Data-Based Models as Basis of a Universally Applicable Energy Management System

et al. 2020

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“…The simulation results of a case study have verified that the algorithm increases the utilization of the ESS and wind turbine while reducing grid dependency. In [7], an optimal ESS control strategy in a grid-connected microgrid has been proposed. Especially, to improve the accuracy of State of Charge (SoC) calculation, the authors have applied an extended ESS model, which utilizes 2D efficiency maps of power and SoC, to the optimization problem.…”

Section: Energy Management In Microgridmentioning

confidence: 99%

“…The term "microgrid", which refers to a localized small electricity grid, can be occasionally disconnected from the traditional centralized grid and operate in an island-mode by leveraging backup energy resources such as ESS, fuel generators, and RES. These research efforts in [5][6][7][8] focused on decreasing dependency on the external grid or balancing between energy supply and demand by increasing ESS utilization in the microgrids. There has also been a considerable increase in the interest in energy management of Vehicle-to-Grid (V2G) systems [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning Based Energy Management Algorithm for Smart Energy Buildings

2018

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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.

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“…Wide attention has been paid to energy management systems (EMS) in microgrids, which may be applied to small-scale nanogrids, although they are not usually focused on prosumer-based microgrids. Literature on microgrids EMS is usually focused on defining the objective function to optimize the economic benefit within the microgrid [3,4]. Energy trading among different agents, like microgrids [5,6] or prosumers [7], has also been studied, with the aim of defining an incentive scheme for energy trading.…”

Section: Introductionmentioning

confidence: 99%

“…In microgrids, ESS usually have functions related to peak shaving and mitigation of intermittent generation fluctuation, typical of some renewable energy sources, like photovoltaic (PV) [20]. ESS management also aims to obtain economic advantages in literature [3,4]. In this paper, the role of ESS focuses on collaborating with DR techniques to outperform the management capability of the nanogrid energy.…”

Section: Introductionmentioning

confidence: 99%

Demand and Storage Management in a Prosumer Nanogrid Based on Energy Forecasting

et al. 2020

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Energy efficiency and consumers’ role in the energy system are among the strategic research topics in power systems these days. Smart grids (SG) and, specifically, microgrids, are key tools for these purposes. This paper presents a three-stage strategy for energy management in a prosumer nanogrid. Firstly, energy monitoring is performed and time-space compression is applied as a tool for forecasting energy resources and power quality (PQ) indices; secondly, demand is managed, taking advantage of smart appliances (SA) to reduce the electricity bill; finally, energy storage systems (ESS) are also managed to better match the forecasted generation of each prosumer. Results show how these strategies can be coordinated to contribute to energy management in the prosumer nanogrid. A simulation test is included, which proves how effectively the prosumers’ power converters track the power setpoints obtained from the proposed strategy.

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Robust Control of a Microgrid Energy Storage System Using Various Approaches

Cited by 39 publications

References 26 publications

Self-Learning Data-Based Models as Basis of a Universally Applicable Energy Management System

Self-Learning Data-Based Models as Basis of a Universally Applicable Energy Management System

Reinforcement Learning Based Energy Management Algorithm for Smart Energy Buildings

Demand and Storage Management in a Prosumer Nanogrid Based on Energy Forecasting

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