Mostafa Kazemi

This paper presents a method for coordinated network expansion planning (CNEP) in which the difference between the total cost and the flexibility benefit is minimized. In the proposed method, the generation expansion planning (GEP) of wind farms is coordinated with the transmission expansion planning (TEP) problem by using energy storage systems (ESSs) to improve network flexibility. To consider the impact of the reactive power in the CNEP problem, the AC power flow model is used. The CNEP constraints include the AC power flow equations, planning constraints of the different equipment, and the system operating limits. Therefore, this model imposes hard nonlinearity onto the problem, which is linearized by the use of first-order Taylor’s series and the big-M method as well as the linearization of the circular plane. The uncertainty of loads, the energy price, and the wind farm generation are modeled by scenario-based stochastic programming (SBSP). To determine the effectiveness of the proposed solution approach, it is tested on the IEEE 6-bus and 24-bus test systems using GAMS software.

Natural gas is increasingly becoming the preferred choice of fuel for flexible electricity generation globally resulting in an electricity system whose reliability is progressively dependent on that of the natural gas transportation system. The cascaded relationship between the reliabilities of these system necessitates an integrated approach to planning both systems. This paper presents a chance constrained programming approach to minimize the investment cost of integrating new natural gas-fired generators, natural gas pipeline, compressors and storage required to ensure desired confidence levels of meeting future stochastic power and natural gas demands. The proposed model also highlights the role of natural gas storage in managing short time uncertainties in developing a long-term expansion plan for both the electric and natural gas systems. A two-stage chance constrained solution algorithm is employed in solving the mix-integer non-linear programming optimization problem and illustrated on a standard IEEE 30 bus test system superimposes on the Belgian high-calorific gas network.

This paper presents a new method for scheduling of battery storage systems for participation in frequency regulation and energy markets, simultaneously. Unknown automatic generation control signal of regulation market is modeled through robust optimization. In addition, the complex effect of participation in regulation market on battery's lifespan is modeled through a dynamic procedure. For this purpose, a long-term optimization process is proposed in which, the short-term participation strategy defines battery's lifespan. In order to prevent fast depreciation of battery due to frequent and deep charges/discharges, a new limiting method is introduced here, which would be useful for participation in regulation market. The proposed long-term model is linearized by implementation of Benders' decomposition. Optimum values for limiting factors are determined in the master problem, while the daily operation strategies are decided by sub-problems. Finally, the applicability of the proposed method is investigated using an illustrative case study.

This paper presents a risk-based approach for evaluating the participation strategy of a battery storage system in multiple markets. Simultaneous offering in day-ahead energy, spinning reserve, and regulation markets is considered in this paper. The uncertainties considered include predicted market prices as well as energy deployment in spinning reserve and regulation markets. A new nonprobabilistic model is introduced in this paper to handle the uncertain nature of spinning reserve and regulation markets. Robust optimization is implemented to model these uncertain parameters and manage their related risk. The proposed risk-based model is a max-min problem, which is converted to its equivalent ordinary maximization problem using duality theory. The presented model is linearized by implementing strong duality theory. Finally, the proposed method is tested and verified using an illustrative case study.

Transmission outage scheduling from a transmission facility owner’s (TFO’s) perspective in a deregulated electricity market has been one of the lesser-explored topics in academia. This paper presents a security-constrained one-step optimal transmission outage scheduling model that can be used by any TFO. The proposed mixed-integer linear programming model incorporates a new load shedding methodology that allows transmission branches to radially connected and in-and-out connected buses to be taken out-ofservice under outage and contingency conditions, which further improves its versatility. The proposed model is tested on two networks, (1) a simple four-bus system and (2) the 62-bus AltaLink 500kV/240kV backbone system in Alberta.

Reliability centered maintenance (RCM) is a cost-effective way to maintaining complex systems, transmission network being one. This paper presents a new method for nonperiodic maintenance scheduling of transmission's elements focusing on failure mode analysis. Adopting a nonperiodic mode-based viewpoint to maintenance, this paper introduces 6 plans for maintenance in each failure mode. The related Markov model for each plan is proposed, too. Given these Markov models, attempts are made to determine the best strategy of maintenance and the optimum rate of inspection and maintenance for each mode. Furthermore, to increase the practicality of the presented method and to reduce the overall maintenance cost, we coordinated the maintenance plans of the failure modes of an element, together. Finally, applicability of the presented method is demonstrated using an illustrative example.

This paper presents a risk-constrained programming approach to solve a retailer's medium-term planning problem. A retailer tries to maximize its profit via determining the optimal price offered to the customers as well as optimal strategy of participating in futures and pool markets. The uncertainty of pool prices is modeled by an envelope-bound information-gap model. Another source of uncertainty in this problem is the clients' demand, which is considered via a scenario generation method. The proposed method is formulated as a bi-level stochastic programming problem based on the information-gap decision theory. The Karush–Kuhn–Tucker optimality conditions are used to convert the bi-level problem into a single-level robust optimization problem. The performance of the proposed method is demonstrated using a case study of the New England market, and results are discussed. Copyright © 2015 John Wiley & Sons, Ltd.