Abstract
Day-ahead electricity pricing is an important strategy for electricity providers to improve grid stability through load scheduling. In this paper, we investigate a general framework for modelling electricity retail pricing based on load demand and market price information. Without any a priori knowledge, we have considered a finite time approach with dynamic system inputs. Our objective is to minimize the average system cost and rebound peaks through energy procurement price, load scheduling and renewable energy source (RES) integration. Initially, the energy consumption cost is calculated based on market clearing price and scheduled load. Then, through reformulation and subsequent modification of optimization problem, we utilize a day-ahead price information to construct individualized price profiles for each user, respectively. To analyse the applicability of proposed pricing policy, analytical solution is obtained which is further validated through comparison with solution obtained from genetic algorithm (GA). From results, it is observed that proposed price policy is non-discriminatory in nature and each user obtained a fair electricity tariff rather than a day-ahead price, which is based on load demand and consumption variation of other users. We also show that optimization problem is sequentially solved with bounded performance guarantee and asymptotic optimality. Finally, simulations are carried in different scenarios; aggregated load and market price, and aggregated load, individualized load, market price and proposed price. Results reveal that our proposed mechanism can charge the price to each user with 23.77% decrease or 5.12% increase based on system requirements.
Highlights
Electricity pricing mechanisms in a day-ahead market charge a fixed price to residential customers for specific time periods
There are two types of demand response (DR) programs being widely used while developing energy management programs; direct load control (DLC) and price based [14], [15]
In order to calculate individualized prices for potential users, we develop a mathematical expression which calculates these prices for each user/unit according to their consumption levels
Summary
Electricity pricing mechanisms in a day-ahead market charge a fixed price to residential customers for specific time periods. The goal of dynamic pricing schemes is to make more efficient utilization of generation capacity through load scheduling, optimization and incentivising. This enables the customers to enhance their consumption level during off and on-peaks hours, without heavily relying on costlier generation and other dependences [2]–[7]. In former, the utility has control to directly turn-off selected load during variation in frequency or overload conditions to maintain the power system stability [16]–[19] These schemes are useful in improving grid stability, this may lead to loss of social welfare and comfort of end users [20]. Some researchers have done a preliminary work on pricing mechanism, there required more appropriate model to customize retails electricity prices in smart grid area
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