The scheduling and operation of power system becomes prominently complex and uncertain, especially with the penetration of distributed power. Load forecasting matters to the effective operation of power system. This paper proposes a novel deep learning framework to forecast the short-term grid load. First, the load data is processed by Box-Cox transformation, and two parameters (electricity pri...

In this paper, a new security-constrained self-scheduling framework incorporating the transmission flow limits in both steady state conditions and post-contingent states is presented to produce efficient bidding strategy for generation companies (GENCOs) in day-ahead electricity markets. Moreover, the proposed framework takes into account the uncertainty of the predicted market prices and model...

An optimal dispatching strategy for a multi-source complementary power generation system taking source–load uncertainty into account is proposed, in order to address the effects of large-scale intermittent renewable energy consumption and load instability on grid dispatching. The problem first converted common situations study, such as forecasting solar wind power. backward scenario reduction L...

The world is facing major challenges related to global warming and emissions of greenhouse gases a causing factor. In 2017, energy industries accounted for 46% all CO2 globally, which shows large potential reduction. This paper proposes novel short-term forecast enable intelligent scheduling flexible electricity consumption minimize the resulting emissions. Two proposed time series decompositio...

Day-ahead energy market clearing relies on a deterministic equivalent model with a limited time horizon, which may lead to inefficient scheduling of generating units from the point of view of generators. For this reason, generators may wish to forgo the profit hedging offered by day-ahead electricity markets and assume the risk of self-committing their units with the hope of securing greater pr...

This paper presents a multi-timescale control strategy to deploy electric vehicle (EV) demand flexibility for simultaneously providing power balancing, grid congestion management, and economic benefits to participating actors. First, an EV charging problem is investigated from consumer, aggregator, and distribution system operator’s perspectives. A hierarchical control architecture (HCA) compri...

The response potential of demand-side resources is becoming increasingly significant in integrated energy system (IES) operations. In addition, to ensure the effective participation devices, their actual responsiveness at different timescales should be considered. Based on these considerations, this paper proposes an IES multi-timescale operation optimization strategy that incorporates multiple...

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...

This paper presents a profit-based model for short-term hydro scheduling adapted to pool-based electricity markets. The objective is to determine a feasible operation of a set of coupled hydro units belonging to a small or medium-size hydroelectric company. The company is assumed to be price-taker, and therefore, market prices are considered exogenous variables and modeled via scenarios. In ord...

In the proposed wind-storage combined operation technology, storage side is foreseen to play a significant role in power system day-ahead generation scheduling. Based on operational characteristics of pumped stations, dispatching method with wind farms and stations studied. The mode that aims at lowest operating cost proposed, taking into consideration coordination relationship between scheduli...