Stochastic Day Ahead Load Scheduling for Aggregated Distributed Energy Resources (original) (raw)
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Electric vehicle grid integration has the potential to stress distribution network equipment and increase peak consumption, unless properly managed. In this paper, we use dynamic programming to develop a decision support algorithm and market participation policy for a load aggregator (LA) managing the charging of plug-in electric vehicles (PEVs) connecting at the same distribution network feeder. The LA submits inflexible and flexible bids to a liberalized hour-ahead power market, while monitoring localized feeder and PEV rate constraints. Flexible bids, which include a bid price or utility, can be cleared as regulation service, cleared as energy, or rejected by the market operator. These market events are probabilistically included within the modeling framework. A case study, based on New York independent system operator data, found that the market participation policy may reduce daily electricity costs for PEVs significantly more than is expected through forecasted electricity price based scheduling.
Electric vehicle grid integration has the potential to stress distribution network equipment and increase peak consumption, unless properly managed. In this paper, we use dynamic programming to develop a decision support algorithm and market participation policy for a load aggregator (LA) managing the charging of plug-in electric vehicles (PEVs) connecting at the same distribution network feeder. The LA submits inflexible and flexible bids to a liberalized hour-ahead power market, while monitoring localized feeder and PEV rate constraints. Flexible bids, which include a bid price or utility, can be cleared as regulation service, cleared as energy, or rejected by the market operator. These market events are probabilistically included within the modeling framework. A case study, based on New York independent system operator data, found that the market participation policy may reduce daily electricity costs for PEVs significantly more than is expected through forecasted electricity price based scheduling.
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Flexible devices with remote monitoring and control availabilities, integrated behind-the-meter or last mile of electricity (grid edge) have significantly become widespread in the past decade. There are emerging distributed energy resource (DER) management solutions that give DER more active roles in power system and energy market operation. DER coordination incorporates inherent uncertainties related to distributed generation from intermittent renewables, non-flexible loads and dynamic prices. Consideration of uncertainties in optimum energy scheduling in community microgrids with a large number of electric vehicles can provide considerable benefits. This study presents a cloud-based optimal energy scheduling approach that considers diverse uncertainties in EV charging coordination as part of community microgrid energy scheduling. A case study is conducted for a representative community microgrid to investigate the benefits and challenges in uncertainty considered optimal energy scheduling.
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IEEE Transactions on Smart Grid, 2015
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Optimal Demand Response Aggregation in Wholesale Electricity Markets
IEEE Transactions on Smart Grid, 2000
Advancements in smart grid technologies have made it possible to apply various options and strategies for the optimization of demand response (DR) in electricity markets. DR aggregation would accumulate potential DR schedules and constraints offered by small-and medium-sized customers for the participation in wholesale electricity markets. Despite various advantages offered by the hourly DR in electricity markets, practical market tools that can optimize the economic options available to DR aggregators and market participants are not readily attainable. In this context, this paper presents an optimization framework for the DR aggregation in wholesale electricity markets. The proposed study focuses on the modeling strategies for energy markets. In the proposed model, DR aggregators offer customers various contracts for load curtailment, load shifting, utilization of onsite generation, and energy storage systems as possible strategies for hourly load reductions. The aggregation of DR contracts is considered in the proposed price-based self-scheduling optimization model to determine optimal DR schedules for participants in day-ahead energy markets. The proposed model is examined on a sample DR aggregator and the numerical results are discussed in the paper.
Day-Ahead Stochastic Scheduling Model Considering Market Transactions in Smart Grids
2018 Power Systems Computation Conference (PSCC), 2018
The integration of renewable generation and electric vehicles (EVs) into smart grids poses an additional challenge to the stochastic energy resource management problem due to the uncertainty related to weather forecast and EVs user-behavior. Moreover, when electricity markets are considered, market price variations cannot be disregarded. In this paper, a twostage stochastic programming approach to schedule the dayahead operation of energy resources in smart grids under uncertainty is presented. A realistic case study is performed using a large-scale scenario with nearly 4 million variables with the goal to minimize expected operation cost of energy aggregators. Three scenarios are analyzed to understand the effect of market transactions and external suppliers on the aggregator model. The results suggest that the market transactions can reduce expected cost, while the external supplier offers risk-free price. In addition, the performance metric shows the superiority of the stochastic approach over an equivalent deterministic model.
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In this study, we address the optimal scheduling of electric vehicle (EV) charging at a multi-point charging station equipped with renewable energy sources and grid energy access. We model the uncertainty of EV arrivals, intermittent renewable energy generation, and fluctuating grid power prices as independent Markov processes. Additionally, the required charging energy for each EV is stochastic. Our objective is to minimize the average waiting time for EVs while adhering to long-term cost constraints. We introduce a queue mapping technique to transform the EV queue into a charging demand queue, establishing equivalence in minimizing their average lengths. We then concentrate on minimizing the charging demand queue length under the long-term cost constraint using a Markov decision process (MDP) framework. The system state encompasses the charging demand queue length, EV arrival patterns, renewable energy storage battery levels, renewable energy arrivals, and grid power prices. Our proposed 2-D policy involves decisions regarding the number of charging demands and energy allocation from the storage battery. We derive necessary conditions for the optimal policy and explore reducing the 2-D policy to focus solely on the number of charging demands. Furthermore, we identify optimal scenarios for charging no demand or maximizing charging demand based on defined sets of system states.