Uncertainty management in multiobjective hydro-thermal self-scheduling under emission considerations

In this paper, a stochastic multiobjective framework is proposed for a day-ahead short-term Hydro Thermal Self-Scheduling (HTSS) problem for joint energy and reserve markets. An efficient linear formulations are introduced in this paper to deal with the nonlinearity of original problem due to the dynamic ramp rate limits, prohibited operating zones, operating services of thermal plants, multi-head power discharge characteristics of hydro generating units and spillage of reservoirs. Besides, system uncertainties including the generating units\u27 contingencies and price uncertainty are explicitly considered in the stochastic market clearing scheme. For the stochastic modeling of probable multiobjective optimization scenarios, a lattice Monte Carlo simulation has been adopted to have a better coverage of the system uncertainty spectrum. Consequently, the resulting multiobjective optimization scenarios should concurrently optimize competing objective functions including GENeration COmpany\u27s (GENCO\u27s) profit maximization and thermal units\u27 emission minimization. Accordingly, the ε-constraint method is used to solve the multiobjective optimization problem and generate the Pareto set. Then, a fuzzy satisfying method is employed to choose the most preferred solution among all Pareto optimal solutions. The performance of the presented method is verified in different case studies. The results obtained from ε-constraint method is compared with those reported by weighted sum method, evolutionary programming-based interactive Fuzzy satisfying method, differential evolution, quantum-behaved particle swarm optimization and hybrid multi-objective cultural algorithm, verifying the superiority of the proposed approach

On the management of wind power intermittency

Nowadays, the world is encountering severe challenges in the energy generation sector. Environmental issues like climate change, global warming and Green House Gases (GHGs) and also social issues like dramatic increase in global population and increasing energy demand are the main causes of global concerns about energy resource management. In this regard, Renewable Energy Sources (RESs) are the suitable substitution to replace the conventional generating units that emit GHGs due to the use of fossil fuels. Among all RESs, wind energy seems to be promising for generating emission-free electrical energy. However, it is naturally unpredictable due to its intermittency which leads to s ome technical problems such as generation imbalance as well as optimal reserve allocation. This paper investigates the solutions to compensate wind intermittency through introducing various technolo gies such as Pumped-Hydro Storage (PH S) units, Plug-in Hybrid Electric Vehicles (PHEVs), solar energy and other electric storages like batteries

Integration of plug-in electric vehicles into microgrids as energy and reactive power providers in market environment

The concept of electricity markets in the deregulated environment generally refers to energy market and reactive power market is not paid attention as much as it deserves to. However, reactive power plays an important role in distribution networks to improve network conditions such as voltage profile improvement and loss reduction. Plug-in electric vehicles (PEVs) are mobile sources of active and reactive power, capable of being participated in energy market, and also in reactive power market without battery degradation. Active and reactive powers are coupled through the ac power flow equations and branch loading limits, as well as PEVs and synchronous generators capability curves. This paper presents a coupled energy and reactive power market in the presence of PEVs. The objective function is threefold, namely offers cost (for energy market), total payment function (for reactive power market), and lost opportunity cost, all to be minimized. The effectiveness of the proposed coupled energy and reactive power market is studied based on a 134-node microgrid with and without PEV participation