A New Algorithm for MINLP Applied to Gas Transport Energy Cost Minimization

It is well documented that both solar photovoltaic (PV) systems and electric vehicles (EVs) positively impact the global environment. However, the integration of high PV resources into distribution networks creates new challenges because of the uncertainty of PV power generation. Additionally, high power consumption during many EV charging operations at a certain time of the day can be stressful for the distribution network. Stresses on the distribution network influence higher electricity tariffs, which negatively impact consumers. Therefore, a home energy management system is one of the solutions to control electricity consumption to reduce electrical energy costs. In this paper, a meta-heuristic-based optimization of a home energy management strategy is presented with the goal of electrical energy cost minimization for the consumer under the time-of-use (TOU) tariffs. The proposed strategy manages the operations of the plug-in electric vehicle (PEV) and the energy storage system (ESS) charging and discharging in a home. The meta-heuristic optimization, namely a genetic algorithm (GA), was applied to the home energy management strategy for minimizing the daily electrical energy cost for the consumer through optimal scheduling of ESS and PEV operations. To confirm the effectiveness of the proposed methodology, the load profile of a household in Udonthani, Thailand, and the TOU tariffs of the provincial electricity authority (PEA) of Thailand were applied in the simulation. The simulation results show that the proposed strategy with GA optimization provides the minimum daily or net electrical energy cost for the consumer. The daily electrical energy cost for the consumer is equal to 0.3847 USD when the methodology without GA optimization is used, whereas the electrical energy cost is equal to 0.3577 USD when the proposed methodology with GA optimization is used. Therefore, the proposed optimal home energy management strategy with GA optimization can decrease the daily electrical energy cost for the consumer up to 7.0185% compared to the electrical energy cost obtained from the methodology without GA optimization.

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Energy cost minimization through optimization of EV, home and workplace battery storage

Science China Technological Sciences ◽

10.1007/s11431-017-9188-y ◽

2018 ◽

Vol 61 (5) ◽

pp. 761-773 ◽

Cited By ~ 2

Author(s):

QianWen Zhong ◽

Stephen Buckley ◽

Anthony Vassallo ◽

YiZe Sun

Keyword(s):

Energy Cost ◽

Cost Minimization ◽

Battery Storage

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Energy Cost Minimization for Internet Data Centers Considering Power Outages

Energy Management of Internet Data Centers in Smart Grid - Green Energy and Technology ◽

10.1007/978-3-662-45676-7_2 ◽

2015 ◽

pp. 13-36

Author(s):

Tao Jiang ◽

Liang Yu ◽

Yang Cao

Keyword(s):

Energy Cost ◽

Data Centers ◽

Cost Minimization ◽

Power Outages

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Data-Driven Design of Control Strategies for Distributed Energy Systems

Journal of Mechanical Design ◽

10.1115/1.4044077 ◽

2019 ◽

Vol 141 (11) ◽

Author(s):

Philip Odonkor ◽

Kemper Lewis

Keyword(s):

Energy Cost ◽

Cost Minimization ◽

Computational Cost ◽

Cost Savings ◽

Price Volatility ◽

Energy Resources ◽

Data Driven ◽

Distributed Energy Resources ◽

Maximization Problem ◽

Distributed Energy

Abstract The flexibility afforded by distributed energy resources in terms of energy generation and storage has the potential to disrupt the way we currently access and manage electricity. But as the energy grid moves to fully embrace this technology, grid designers and operators are having to come to terms with managing its adverse effects, exhibited through electricity price volatility, caused in part by the intermittency of renewable energy. With this concern however comes interest in exploiting this price volatility using arbitrage—the buying and selling of electricity to profit from a price imbalance—for energy cost savings for consumers. To this end, this paper aims to maximize arbitrage value through the data-driven design of optimal operational strategies for distributed energy resources (DERs). Formulated as an arbitrage maximization problem using design optimization principles and solved using reinforcement learning, the proposed approach is applied toward shared DERs within multi-building residential clusters. We demonstrate its feasibility across three unique building clusters, observing notable energy cost reductions over baseline values. This highlights a capability for generalized learning across multiple building clusters and the ability to design efficient arbitrage policies for energy cost minimization. The scalability of this approach is studied using two test cases, with results demonstrating an ability to scale with relatively minimal additional computational cost, and an ability to leverage system flexibility toward cost savings.

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Designing Optimal Arbitrage Policies for Distributed Energy Systems in Building Clusters Using Reinforcement Learning

Volume 2A: 45th Design Automation Conference ◽

10.1115/detc2019-97190 ◽

2019 ◽

Author(s):

Philip Odonkor ◽

Kemper Lewis

Keyword(s):

Renewable Energy ◽

Reinforcement Learning ◽

Electricity Markets ◽

Energy Cost ◽

Cost Minimization ◽

Control Strategies ◽

Cost Savings ◽

Price Volatility ◽

Maximization Problem ◽

Distributed Energy

Abstract In the wake of increasing proliferation of renewable energy and distributed energy resources (DERs), grid designers and operators alike are faced with several emerging challenges in curbing allocative grid inefficiencies and maintaining operational stability. One such challenge relates to the increased price volatility within real-time electricity markets, a result of the inherent intermittency of renewable energy. With this challenge, however, comes heightened economic interest in exploiting the arbitrage potential of price volatility towards demand-side energy cost savings. To this end, this paper aims to maximize the arbitrage value of electricity through the optimal design of control strategies for DERs. Formulated as an arbitrage maximization problem using design optimization, and solved using reinforcement learning, the proposed approach is applied towards shared DERs within multi-building residential clusters. We demonstrate its feasibility across three unique building cluster demand profiles, observing notable energy cost reductions over baseline values. This highlights a capability for generalized learning across multiple building clusters and the ability to design efficient arbitrage policies towards energy cost minimization. Finally, the approach is shown to be computationally tractable, designing efficient strategies in approximately 5 hours of training over a simulation time horizon of 1 month.

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