Toward the Minimization of the Levelized Energy Costs of Microgrids Using Both Long-Term and Short-Term Storage Devices

François-Lavet, Vincent; Gemine, Quentin; Ernst, Damien; Fonteneau, Raphaël

doi:10.1201/b19664-17

Cited by 15 publications

(11 citation statements)

References 10 publications

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“…The algorithm offers no practical optimality guarantees, and even if the global optimum was reached, the resulting design would be optimal only with respect to a rule-based operational strategy, which is not economically and technically-optimal. A microgrid featuring solar PV generation along with both short-term and long-term storage in the form of batteries and hydrogen tanks is considered in [10]. Optimal technology sizes are identified via a multi-year LP formulation, but no dispatchable technologies or methanation are considered.…”

Section: Related Workmentioning

confidence: 99%

Centralised Planning of National Integrated Energy System with Power-to-Gas and Gas Storages

Berger

Radu

Fonteneau

et al. 2018

Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion (MEDPOWER 2018)

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This paper proposes an optimisation-based framework to tackle long-term centralised planning problems of integrated energy systems with bi-directional electricity-gas carriers coupling under various policy constraints. The framework is leveraged to gain insight into possible configurations of the future Belgian energy system, and identify the cost-optimal energy mix as well as short and long-term storage requirements to satisfy CO2 emissions reductions and energy security targets. Results shed light on the economics of a transition to a low-carbon energy system and reveal the potential of power-togas and storage in gas form to help achieve ambitious emissions reduction goals.

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Section: Related Workmentioning

confidence: 99%

Centralised Planning of National Integrated Energy System with Power-to-Gas and Gas Storages

Berger

Radu

Fonteneau

et al. 2018

Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion (MEDPOWER 2018)

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“…More than 160 microgrid project are currently active world-wide (see http://www.resilientproject.eu/). A growing research community is investigating business cases related with electricity microgrids François-Lavet et al (2016a).…”

Section: The Future Of Electric Power Systems: Even More Control and mentioning

confidence: 99%

Reinforcement Learning for Electric Power System Decision and Control: Past Considerations and Perspectives

2017

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Abstract:In this paper, we review past (including very recent) research considerations in using reinforcement learning (RL) to solve electric power system decision and control problems. The RL considerations are reviewed in terms of specific electric power system problems, type of control and RL method used. We also provide observations about past considerations based on a comprehensive review of available publications. The review reveals the RL is considered as viable solutions to many decision and control problems across different time scales and electric power system states. Furthermore, we analyse the perspectives of RL approaches in light of the emergence of new-generation, communications, and instrumentation technologies currently in use, or available for future use, in power systems. The perspectives are also analysed in terms of recent breakthroughs in RL algorithms (Safe RL, Deep RL and path integral control for RL) and other, not previously considered, problems for RL considerations (most notably restorative, emergency controls together with so-called system integrity protection schemes, fusion with existing robust controls, and combining preventive and emergency control).

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“…In RL problems, the goal is to find an optimal policy, h * , that minimizes the sum of costs or penalties over the entire optimization horizon, Equation (6).…”

Section: Problem Formulationmentioning

confidence: 99%

“…Microgrids can be a good base for the study and implementation of smart grid solutions [3][4][5]. Microgrids are electrical systems consisting of loads and distributed energy resources (like energy storage facilities and RES) that can operate in parallel with or disconnected from the main utility grid [6]. It is expected that the future power grid will be a combination of multiple microgrids collaborating with each other [7,8].…”

Section: Introductionmentioning

confidence: 99%

Battery Energy Management in a Microgrid Using Batch Reinforcement Learning

et al. 2017

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Abstract:Motivated by recent developments in batch Reinforcement Learning (RL), this paper contributes to the application of batch RL in energy management in microgrids. We tackle the challenge of finding a closed-loop control policy to optimally schedule the operation of a storage device, in order to maximize self-consumption of local photovoltaic production in a microgrid. In this work, the fitted Q-iteration algorithm, a standard batch RL technique, is used by an RL agent to construct a control policy. The proposed method is data-driven and uses a state-action value function to find an optimal scheduling plan for a battery. The battery's charge and discharge efficiencies, and the nonlinearity in the microgrid due to the inverter's efficiency are taken into account. The proposed approach has been tested by simulation in a residential setting using data from Belgian residential consumers. The developed framework is benchmarked with a model-based technique, and the simulation results show a performance gap of 19%. The simulation results provide insight for developing optimal policies in more realistically-scaled and interconnected microgrids and for including uncertainties in generation and consumption for which white-box models become inaccurate and/or infeasible.

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Toward the Minimization of the Levelized Energy Costs of Microgrids Using Both Long-Term and Short-Term Storage Devices

Cited by 15 publications

References 10 publications

Centralised Planning of National Integrated Energy System with Power-to-Gas and Gas Storages

Centralised Planning of National Integrated Energy System with Power-to-Gas and Gas Storages

Reinforcement Learning for Electric Power System Decision and Control: Past Considerations and Perspectives

Battery Energy Management in a Microgrid Using Batch Reinforcement Learning

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