20,628 research outputs found
Decentralized Greedy-Based Algorithm for Smart Energy Management in Plug-in Electric Vehicle Energy Distribution Systems
Variations in electricity tariffs arising due to stochastic demand loads on the power grids have stimulated research in finding optimal charging/discharging scheduling solutions for electric vehicles (EVs). Most of the current EV scheduling solutions are either centralized, which suffer from low reliability and high complexity, while existing decentralized solutions do not facilitate the efficient scheduling of on-move EVs in large-scale networks considering a smart energy distribution system. Motivated by smart cities applications, we consider in this paper the optimal scheduling of EVs in a geographically large-scale smart energy distribution system where EVs have the flexibility of charging/discharging at spatially-deployed smart charging stations (CSs) operated by individual aggregators. In such a scenario, we define the social welfare maximization problem as the total profit of both supply and demand sides in the form of a mixed integer non-linear programming (MINLP) model. Due to the intractability, we then propose an online decentralized algorithm with low complexity which utilizes effective heuristics to forward each EV to the most profitable CS in a smart manner. Results of simulations on the IEEE 37 bus distribution network verify that the proposed algorithm improves the social welfare by about 30% on average with respect to an alternative scheduling strategy under the equal participation of EVs in charging and discharging operations. Considering the best-case performance where only EV profit maximization is concerned, our solution also achieves upto 20% improvement in flatting the final electricity load. Furthermore, the results reveal the existence of an optimal number of CSs and an optimal vehicle-to-grid penetration threshold for which the overall profit can be maximized. Our findings serve as guidelines for V2G system designers in smart city scenarios to plan a cost-effective strategy for large-scale EVs distributed energy management
Using Battery Storage for Peak Shaving and Frequency Regulation: Joint Optimization for Superlinear Gains
We consider using a battery storage system simultaneously for peak shaving
and frequency regulation through a joint optimization framework which captures
battery degradation, operational constraints and uncertainties in customer load
and regulation signals. Under this framework, using real data we show the
electricity bill of users can be reduced by up to 15\%. Furthermore, we
demonstrate that the saving from joint optimization is often larger than the
sum of the optimal savings when the battery is used for the two individual
applications. A simple threshold real-time algorithm is proposed and achieves
this super-linear gain. Compared to prior works that focused on using battery
storage systems for single applications, our results suggest that batteries can
achieve much larger economic benefits than previously thought if they jointly
provide multiple services.Comment: To Appear in IEEE Transaction on Power System
Control and Communication Protocols that Enable Smart Building Microgrids
Recent communication, computation, and technology advances coupled with
climate change concerns have transformed the near future prospects of
electricity transmission, and, more notably, distribution systems and
microgrids. Distributed resources (wind and solar generation, combined heat and
power) and flexible loads (storage, computing, EV, HVAC) make it imperative to
increase investment and improve operational efficiency. Commercial and
residential buildings, being the largest energy consumption group among
flexible loads in microgrids, have the largest potential and flexibility to
provide demand side management. Recent advances in networked systems and the
anticipated breakthroughs of the Internet of Things will enable significant
advances in demand response capabilities of intelligent load network of
power-consuming devices such as HVAC components, water heaters, and buildings.
In this paper, a new operating framework, called packetized direct load control
(PDLC), is proposed based on the notion of quantization of energy demand. This
control protocol is built on top of two communication protocols that carry
either complete or binary information regarding the operation status of the
appliances. We discuss the optimal demand side operation for both protocols and
analytically derive the performance differences between the protocols. We
propose an optimal reservation strategy for traditional and renewable energy
for the PDLC in both day-ahead and real time markets. In the end we discuss the
fundamental trade-off between achieving controllability and endowing
flexibility
Definition and evaluation of model-free coordination of electrical vehicle charging with reinforcement learning
Demand response (DR) becomes critical to manage the charging load of a growing electric vehicle (EV) deployment. Initial DR studies mainly adopt model predictive control, but models are largely uncertain for the EV scenario (e.g., customer behavior). Model-free approaches, based on reinforcement learning (RL), are an attractive alternative. We propose a new Markov decision process (MDP) formulation in the RL framework, to jointly coordinate a set of charging stations. State-of-the-art algorithms either focus on a single EV, or control an aggregate of EVs in multiple steps (e.g., 1) make aggregate load decisions and 2) translate the aggregate decision to individual EVs). In contrast, our RL approach jointly controls the whole set of EVs at once. We contribute a new MDP formulation with a scalable state representation independent of the number of charging stations. Using a batch RL algorithm, fitted -iteration, we learn an optimal charging policy. With simulations using real-world data, we: 1) differentiate settings in training the RL policy (e.g., the time span covered by training data); 2) compare its performance to an oracle all-knowing benchmark (providing an upper performance bound); 3) analyze performance fluctuations throughout a full year; and 4) demonstrate generalization capacity to larger sets of charging stations
Probably Approximately Correct Nash Equilibrium Learning
We consider a multi-agent noncooperative game with agents' objective
functions being affected by uncertainty. Following a data driven paradigm, we
represent uncertainty by means of scenarios and seek a robust Nash equilibrium
solution. We treat the Nash equilibrium computation problem within the realm of
probably approximately correct (PAC) learning. Building upon recent
developments in scenario-based optimization, we accompany the computed Nash
equilibrium with a priori and a posteriori probabilistic robustness
certificates, providing confidence that the computed equilibrium remains
unaffected (in probabilistic terms) when a new uncertainty realization is
encountered. For a wide class of games, we also show that the computation of
the so called compression set - a key concept in scenario-based optimization -
can be directly obtained as a byproduct of the proposed solution methodology.
Finally, we illustrate how to overcome differentiability issues, arising due to
the introduction of scenarios, and compute a Nash equilibrium solution in a
decentralized manner. We demonstrate the efficacy of the proposed approach on
an electric vehicle charging control problem.Comment: Preprint submitted to IEEE Transactions on Automatic Contro
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