Ocean energy, as a renewable resource that covers a large area of the earth, is increasingly used for power generation in forms of wave, tidal and thermal. Ocean thermal energy conversion (OTEC) systems utilize from the difference between the temperatures of surface and deep water and drive a thermodynamic Rankine cycle to generate electric power. The generated power of these plants depends on the temperature of the surface water as warm source. Due to the variation in the temperature of ocean surface, the output power of the OTEC system frequently changes in the year. This uncertainty nature results in the variation in the generated power and so, integration of large-scale OTEC generation units to the power system is a challenging problem. Thus, new techniques must be developed to study the effects of these variable resources on the power system. In operation studies of power systems, the balance between generation and consumption is important and from reliability point of view, some spinning reserve must be scheduled to prevent load curtailment in the events such as forced outages of generation units, transmission lines and so on. In a power system containing large-scale OTEC power plants, the uncertainty nature of these plants must be considered in the reserve scheduling. Thus, for this purpose, a reliability model considering both failure of composed components and variation in the output power, is developed for OTEC units. To determine a suitable multi-state model for OTEC units, fuzzy c-means clustering technique and XB index are utilized. Then, the proposed multi-state model is used to determine spinning reserve value of a power system containing OTEC plants using of the modified PJM method. Numerical results associated to RBTS and IEEE-RTS present the effectiveness of the proposed technique for operation studies of power systems containing OTEC systems.

In this paper a joint energy and reserve market with transmission switching considering dynamic constraints has been proposed to minimize the cost of supplying load, reliability expenses and avoidance of transient instability in line switching.Transmission switching is used during contingencies and steady state to determine optimal required energy and reserve values. To investigate the efficiency of the proposed strategy IEEE 14 bus test is studied. According to the obtained results, this strategy decreases energy and reserve marginal prices, as well as reliability cost.

The completely Electric Power System encompasses three parts: Generation, Transmission, and Distribution that all require maintenance to better system reliability and energy efficiency. Most generation maintenance scheduling (GMS) packages consider preventive maintenance scheduling for generating units over one or two year's time horizon to lessen the total operation costs while fulfilling system energy requirements. In advanced power systems, the inclusion of network limitations, reserve index, forced outage rates of the units, and demand for electricity have highly increased with related expansions in system size, which have led to the higher number of generators and lower reserve margins that making the generator maintenance scheduling problem more complex. This paper proposes a security constrained model for preventive generation maintenance scheduling problem. For more realistic study, system reliability indices such as transmission security, manpower constraint, and forced outage rate of the system as well as amount of system reserve are considered for the proposed maintenance scheduling problem. Impact of the load curve on GMS problem is investigated by a novel proposed penalty factor. General Algebraic Modeling System (GAMS) is utilized for solving optimization problem. An IEEE 24-bus test system is employed for simulation and show the accuracy of results.

Due to uncertain nature of wind and photovoltaic power units, the participation of this units in electricity markets is subjected to significant deviation penalties. This issue leads to despondency or even admission of these units in the competitive environment. With regard to this fact that the low deviations are available when predictions are performed in a short-term horizon and also distributed generation (DG) units have several potential benefits to provide ancillary services, in this article the participation of DG units in intra-market ancillary services is investigated. The intra-day market consists of 3-8 hours scheduled horizon time and will lead to reduction in deviations. Here, three kinds of uncertainties, consist of renewable DG unit’s output, load and price of electricity markets will be predicted by using an adaptive neuro-fuzzy inference system (ANFIS). The proposed method is optimized by Genetic Algorithm (GA) and is tested on a test system. The results supported the efficiency of proposed method.

Strategic bidding in joint energy and spinning reserve markets is a challenging task from the viewpoint of Generation Companies (GenCos). In this paper, the interaction between energy and spinning reserve markets is modeled considering a joint probability density function for the prices of these markets. Considering pay-as-bid pricing mechanism, the bidding problem is formulated and solved as a classic optimization problem. The results show that the contribution of a GenCo in each market strongly depends on its production cost and its level of risk-aversion. Furthermore, if reserve bid acceptance is considered subjected to winning in the energy market, it can affect the strategic bidding behavior.

In this paper, the effect of a battery energy storage system (BESS) as a spinning reserve is considered to control the frequency of a ‎microgrid consisting of a diesel generator, photovoltaic, BESS, and electrical loads. In this stand-alone microgrid, the output power of ‎diesel generators and the BESS are subject to variations to compensate for power fluctuations caused by the load and output power of ‎photovoltaic. Therefore, secondary control, in addition to the primary control, has been proposed to deal with frequency deviation and ‎accelerate the operation of spinning reserve. The scheme is simulated in a hybrid power plant, where results show the effectiveness of the ‎secondary control on frequency deviation damping of the microgrid, thus improving dynamic stability.

In the electricity market, generation company attempts to maximize their profit in a bidding strategy approach. As the transactions of power and spinning reserve are done in a transmission network, consideration of transmission constraints and spinning reserve uncertainties becomes necessary. In the bidding strategy problem, there are various demand uncertainties. Usually, electricity markets consider a fixed spinning reserve with fixed request probability to ensure that demand is met. However, the actual spinning reserve is stochastic in quantity and requests hours that should be modeled and simulated. Another demand uncertainty is demand response programs include various stochastic types. One of the most famous demand response programs is electric vehicle parking with stochastic charging/discharging amounts and hours. The objection of this study is solving the bidding strategy problem considering transmission constraints, spinning reserve uncertainty, and electric vehicle parking as a demand response program based on a heuristic approach. An actual spinning reserve model using normal distribution is proposed and three case studies are presented. In the first case, improvement in profit of the generation company by 4.15-47.95% and 20.84-31.30% under single and double-sided auctions are reached, respectively. Where transmission constraints and spinning reserve uncertainty are considered, the optimal bidding strategy problem is solved in the energy and spinning reserve market for three-generation companies in the IEEE 6-bus network where transmission constraints are satisfied at all scenarios of spinning reserve requests. When electric vehicle parking is considered, it is shown that demand response programs have direct effects of bidding parameters such as market clearing price, generation companies power awarded and profits.