Employing demand response in energy procurement plans of electricity retailers

Highlights

• A new method is proposed to consider demand response as a supplier of retailers.

• To model demand response, a reward-based DR is mathematically formulated.

• A scenario-based factor is modeled to consider the uncertainty of customers.

• Results show the feasibility of employing DR as an energy provider of retailers.

Abstract

This paper proposes a new framework in which demand response (DR) is incorporated as an energy resource of electricity retailers in addition to the commonly used forward contracts and pool markets. In this way, a stepwise reward-based DR is proposed as a real-time resource of the retailer. In addition, the unpredictable behavior of customers participating in the proposed reward-based DR is modeled through a scenario-based participation factor. The overall problem is formulated as a stochastic optimization approach in which pool prices and customers’ participation in DR are uncertain variables. The feasibility of the problem is evaluated on a realistic case of the Australian National Electricity Market (NEM) and solved using General Algebraic Modeling System (GAMS) software.

Introduction

Demand Response (DR) is defined as changes in electricity usages of consumers as a response to new price tariffs and/or offered incentives [1]. Many studies have been presented to explore the basic concepts, classifications, and technical aspects of DR. The definition of DR programs are addressed in [1]. Additionally, this reference introduces various DR programs and categorizes them into two groups, namely incentive- and price-based DR. Incentive-based DR programs are formulated in several papers such as [2], [3], [4]. Ref. [2] provides the mathematical formulations of interruptible load services. A coupon-based method is formulated in [3] where the incentive offered to consumers is determined according to market prices. An incentive-based scheme is presented in [4] through which both the energy cost and peak-to-average ratio are minimized using a game theory approach. Price-based DR actions are also presented in many researches such as [5], [6], [7], [8]. Paper [5] models a real-time pricing approach for smart grid applications. Authors in [6] address the elasticity concept which reflects the responsiveness of customers to price changes. A comprehensive time-of-use model is formulated in [7] where the elasticity is considered as a non-zero cross and flexible function. A commercial DR concept is introduced in [8] in order to study DR impacts on the power market. Finally, the detailed control strategies of managing electrical loads such as water heater systems, air conditioners, space heating and cooling systems are provided in [9], [10], [11], [12], [13].

Electricity retailers are intermediary companies which buy electricity from wholesale markets and resell it to consumers. They procure the required energy mainly from pool markets and bilateral contracts. Another useful energy resource which can be employed by retailers is DR. However, a few studies in the literature address this concept. Authors in [14] use interruptible loads to alleviate the uncertainty of pool markets faced by a load serving entity. Two interruptible load contracts, pay-in-advance and pay-as-you-go, are evaluated in [15] as suppliers of retailers. Self-production is also used in [16] to limit the risk of cost fluctuations in pool markets. Ref. [17] uses interruptible loads as an energy resource of distribution companies. A short-term deterministic model is presented in [18] where distribution companies can use interruptible loads to bid into the market. Authors in [19] use interruptible programs in short-term decisions of retailers. Besides interruptible loads, real-time pricing and time-of-use are also offered by retailer to alter the electricity usage of consumers [20].

This paper proposes a stepwise reward-based DR in which the uncertainty of customers’ behavior is modeled through a scenario-based participation factor. A medium-term energy procurement framework is proposed for retailers in which they employ the reward-based DR in addition to forward contracts and a pool market. A stochastic programming approach is formulated where both pool prices and customers’ behavior are considered as uncertain variables. The overall problem is mixed-integer linear programming in which the risk is carried out using Conditional Value-at-Risk (CVaR). The feasibility of the proposed method is evaluated using the market data of the Queensland region within the Australian NEM.

Generally, the contributions of this paper are summarized as follows:

• A stepwise reward-based DR is mathematically formulated where the unpredictable behavior of customers participating in this DR program is modeled through scenario-based participation factor.

• A stochastic energy procurement problem is proposed in which the proposed reward-based DR is employed as a real-time energy resource of electricity retailers.