Pricing algorithms for the day-ahead electricity market with flexible consumer participation

Abstract

In this paper, we consider the design of the dayahead market for the smart electrical grid. Consumers with flexible demand and generator companies participate in the market to settle on their load and supply schedules, respectively. The market is operated by an Independent System Operator (ISO) whose purpose is to maximize social welfare while keeping load and supply balanced in the electricity network. We develop two distributed pricing algorithms that achieve optimum welfare. The first algorithm yields time-dependent market prices under convexity assumptions on utility and cost functions and the second algorithm yields bundle prices for arbitrary utility and cost functions. In both algorithms, flexible consumers and generator companies simply determine their own schedules based on the prices updated by the ISO at each iteration. We show that the participation of flexible demand in the day-ahead market reduces supply volatility, which would be present when flexible demand does not take part in price setting procedure.

Optimization Models and Methods for Demand-Side Management of Residential Users: A Survey

“…In some works [42], energy-based and comfort-based devices are often combined, thus defining appliances characterized by both a certain amount of energy E E EB ,T OT e to be consumed over a set of time slots N E EB e and a comfort function, which defines the comfort of users based on how this energy amount is allocated over slots N E EB e . In order to represent the aggregate power demand of all devices (i.e., fixed, shiftable and elastic), the non-negative variables p T OT n can be introduced, which are defined as follows:…”

“…Cooperative models can also be used to study electricity markets with users' participation as in [42]. In this work, a day-ahead market is proposed for the settlement of next-day electricity prices.…”

Genetic algorithm‐based non‐linear auto‐regressive with exogenous inputs neural network short‐term and medium‐term uncertainty modelling and prediction for electrical load and wind speed

“…Moreover, utilities can maximise its payoff, while consumers can reduce and manage the load for cost savings. Furthermore, independent system operators can properly control its generation scheduling, system resources, and maintaining healthy economic growth of power system [2].…”