Meeting peak electricity demand through combinatorial reverse auctioning of renewable energy

Abstract

The option of organizing E-auctions to purchase electricity required for anticipated peak load period is a new one for utility companies. To meet the extra demand load, we develop electricity combinatorial reverse auction (CRA) for the purpose of procuring power from diverse energy sources. In this new, smart electricity market, suppliers of different scales can participate, and homeowners may even take an active role. In our CRA, an item, which is subject to several trading constraints, denotes a time slot that has two conflicting attributes, electricity quantity and price. To secure electricity, we design our auction with two bidding rounds: round one is exclusively for variable energy, and round two allows storage and nonintermittent renewable energy to bid on the remaining items. Our electricity auction leads to a complex winner determination (WD) task that we represent as a resource procurement optimization problem. We solve this problem using multi-objective genetic algorithms in order to find the trade-off solution that best lowers the price and increases the quantity. This solution consists of multiple winning suppliers, their prices, quantities and schedules. We validate our WD approach based on large-scale simulated datasets. We first assess the time-efficiency of our WD method, and we then compare it to well-known heuristic and exact WD techniques. In order to gain an exact idea about the accuracy of WD, we implement two famous exact algorithms for our constrained combinatorial procurement problem.

A Computationally Efficient Method for Energy Allocation in Spot Markets With Application to Transactive Energy Systems

“…The economic models based on bidding and allocation problems were vastly studied in the literature for computing and resource management [33]. The multi-objective genetic algorithm was proposed in [34] for combinatorial reverse auction of renewable energy. Here, the computation time and accuracy results were compared with the branch-and-bound heuristic method.…”

Availability estimation of wind power forecasting and optimization of day-ahead unit commitment

“…With the ongoing global petrochemical energy depletion, environmental degradation is an increasingly serious situation. The global Energy Internet, with renewable energy, has emerged as the main energy supply form and electric energy as the main energy carrier, which is an inevitable trend for the development of human energy system and energy industry [1][2][3]. In addition, such power generations with renewable energy such as wind power and photovoltaic power, have transformed the pattern of modern power system and the form of power generation [4].…”

“…According to durations of the load peaks of the power grid, the scenario mode is d =(1,2,5,10,15,18,20, 25, 30, 60), in terms of minutes. Based on the historical forecasting data, the probability vector with the wind power forecasting error e i = 10% in each scenario is p 0 = (0.80, 0.82, 0.85, 0.81, 0.93, 0.85, 0.91, 0.93, 0.91, 0.93) T .…”