Stochastic optimization for retailers with distributed wind generation considering demand response

Abstract

In this paper, a multi-stage stochastic model is presented for a renewable distributed generation (RDG)-owning retailer to determine the trading strategies existing in a competitive electricity market. Uncertainties associated with wholesale electricity market price, clients' consumption and power output of wind resources are considered through auto regressive integrated moving average (ARIMA) approach. In the proposed method, three trading floors are addressed for the retailer to hedge against the uncertainties. In the first stage, the retailer participates in day-ahead market to supply the clients and in the second stage, intraday market is addressed to allow the retailer to modify the schedule of its clients' consumption/RDG production. Due to unfavorable uncertainties, especially in renewable power production, real-time market is considered in the third stage to diminish the uncertainty at power delivery time. Cost function of wind resources considering capital, operation and maintenance (O&M) cost is incorporated in the objective function to increase the applicability of the mechanism. The proposed approach is formulated for risk-averse and risk-taker retailer through conditional value at risk (CVaR) approach. In order to study the impact of retail strategies on consumption pattern and consumers' electricity bills, time-of-use (TOU) demand response programs are discussed in this paper. Formulating the problem, the mixed integer non-linear programming (MILNP) problem is transformed into mixed integer linear programming (MILP) by jointly using decomposition and disjunctive constraints. Finally, a case study containing wind power resources, energy storage system and retailer is considered to analyze the proficiency of the proposed approach.

Bilevel programming approach to demand response management with day-ahead tariff

“…In [20], the problem of dynamic pricing for DRM is formulated as a Markov decision process, and reinforcement learning is used to solve it. In [21], demand response is modeled by directly quantifying the delay-tolerant demand and its dependence on price by linear, potential, exponential and logarithmic load functions.…”

Risk‐based retailer profit maximization: Time of Use price setting for elastic demand

“…Objective (27) is maximized subject to constraints on bilateral contract (3), self-generating (4) to (17), spot market (19), energy balance (20), sale price (22) to (24), and demand redistribution constraint (25). Here, sale price, price-responsive demand, and portfolio of energy to be procured from various options are key decision variables of the problem.…”

“…A retailer with RE self-generation makes decisions targeting best utilization of RE availability, to maximize its profit. 24 The presence of multiple RE sources such as wind and solar would impact the optimal trading strategy of the retailer 25,26 than with a single RE source. In this perspective, this paper determines optimal Time of Use (ToU) sale prices to be offered and optimal energy procurement portfolio, for a retailer with and without RE, considering elastic demand and wholesale market price uncertainty, aiming to maximize retailer's profit.…”

“…This exposes retailers to price risk, which needs to be considered while making procurement decisions. 24 The presence of multiple RE sources such as wind and solar would impact the optimal trading strategy of the retailer 25,26 than with a single RE source. 9,10 Further, demand shifting over different hours significantly impacts its energy procurement cost, price risk, and eventually the offered sale price.…”

Distributed generation planning for diversified participants in demand response to promote renewable energy integration

“…Moreover, the DSO could make concession on its current profit to achieve the optimal longterm development plan. This approach is significantly superior to the previous studies focusing on the interests of consumers [29,30] or the profits of retailers [31], as those studies could not avoid falling into sub-optimum due to the short-sightedness of consumers/retailers.…”