The electricity market has evolved from a regulated monopoly to a more liberalized competitive market, which allows a generating company (GENCO) to bid to provide energy. The two-period structure of the electricity market (day-ahead and real-time market) introduces a mechanism for determining the GENCO’s optimal bidding strategy. The difference between clearing prices for each period adds uncer...

This paper presents a new methodology to maximise the income and derive the optimal end of day storage of closed-loop and daily-cycle pumped-storage hydropower plants. The plants participate in the day-ahead energy market as a price-taker and in the secondary regulation reserve market as a price-maker, in the context of the Iberian electricity system. The real-time use of the committed reserves...

Traditional management of electricity portfolios is focused on the day-ahead market and futures of longer maturity. Market participants use these markets to trade any excess production or consumption in their portfolio and balance their total position. Within limits, market participants can also resort to the balancing energy market to close their positions. The energy transactions on the balan...

This paper proposes an agent-based computational model of the Italian wholesale electricity market where Generation Companies (GenCos) sign forward contracts and then accordingly submit bids in a Day-Ahead spot Market (DAM). The DAM clearing mechanism considers realistic physical constraints of the Italian high-voltage transmission network, thus determining realistic zonal market prices. The gl...

This paper proposes a probabilistic optimization method that produces optimal bidding curves to be submitted by an aggregator to the day-ahead electricity market and the intraday market, considering the flexible demand of his customers (based in time dependent resources such as batteries and shiftable demand) and taking into account the possible imbalance costs as well as the uncertainty of for...

In today’s restructured environment, congestion management plays an essential role in power system operation. Different methods are presented and discussed in this respect for congestion management in short-term and long-term intervals. It is attempted in the present paper to investigate the impact mechanism of FACTS devices and demand response programs together with generation re-dispatch as s...

Day-Ahead Market offers electricity market participants the opportunity to trade one day ahead of real-time. For each hour, a separate Clearing Price is created in Market. This study aims predict hourly using deep learning techniques. In this context, 24-hour Prices were forecasted with MLP, CNN, LSTM, and GRU. LSTM had best average forecasting performance an 8.15 MAPE value, according results ...

In this paper, we propose a dynamic market mechanism that converges to the desired market equilibrium. Both locational marginal prices and the schedules for generation and consumption are determined through a negotiation process between the key market players. In addition to incorporating renewables, this mechanism accommodates both consumers with a shiftable Demand Response and an adjustable D...

Modelling and forecasting Stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. This nonlinearity affects the efficiency of the price characteristics. Using an Artificial Neural Network (ANN) is a proper way to model this nonlinearity and it has been used successfully in one-step-ahead and multi-step-ahead prediction of di...