Abstract
With the deepening of electricity market reform in China, the competition in the electricity retail market becomes increasingly intense. Electricity retailers (ERs) need to explore new business models to enhance their competitiveness in the retail market. Meanwhile, with the improvement of industrial production and people's living standards, more and more nonlinear electrical equipment have been put into use, leading to severe harmonic pollution problems. Harmonic pollution causes loss of electricity, resulting in the economic loss of customers, especially for large industrial customers. In the above contexts, this paper proposes a novel business model that incorporates harmonic control as a value-added service into electricity retail contracts for utility-owned ERs. Both utility-owned ERs and customers can benefit from the designed business model. For customers, it helps them to improve the power quality while saving the electricity cost. For ERs, it helps them to cultivate the customer loyalty and improve the customer satisfaction. A case study is performed to demonstrate the effectiveness of the proposed business model.

Abstract
Secure and reliable operation is one of the main challenges in restructured power systems. Wind energy has been gaining increasing global attention as a clean and economic energy source, despite the operational challenges its intermittency brings. In this study, we present a formulation for electricity and reserve market clearance in the presence of wind farms. Uncertainties associated with generation and line outages are modeled as different system scenarios. The formulation incorporates the cost of different scenarios in a two-stage short-term (24-hours) clearing process, also considering different types of reserve. The model is then linearized in order to be compatible with standard mixed-integer linear programming solvers, aiming at solving the security constrained unit-commitment problem using as few variables and optimization constraints as possible. As shown, this will expedite the solution of the optimization problem. The model is validated by testing it on a case study based on the IEEE RTS1, for which results are presented and discussed.

Abstract
The electric spring (ES) is a novel voltage compensator which is series with a non-critical load to regulate the critical load voltage. The voltage fluctuation is caused by wind speed fluctuation, load fluctuation, and generator tripping. In busbar voltage drop situation, the electric spring decreases the voltage of non-critical load in order to support the critical load (busbar) voltage. All the non-critical loads couldn't work under any voltage (for example 0.5 pu). In this paper, a control strategy founded on active and reactive power compensations has been proposed for voltage control of critical loads on a reference value while it controls the voltage of non-critical loads between an acceptable boundary. The proposed controller has two voltage control loops which adjusts active and reactive power of the electric spring. The experimental results from the case study show that the ES with the proposed control strategy can effectively mitigate double voltage control of both critical and non-critical loads while dynamically managing the demand response of the system at the same time.

Abstract
This paper represents a model for finding the strategic bidding equilibrium of a virtual power plant in a joint energy and regulation market in the presence of rivals. A bi-level mathematical program with equilibrium constraints (MPEC) is represented for modeling the behavior of each producer. The upper level deals with profit maximization of each strategic unit and the lower level encompasses social welfare maximization. This is the first objective of the presented model. Power transfer distribution factors (PTDFs) are employed to model transmission constraints. The proposed bi-level problem is converted to a traceable mixed integer linear programming problem using duality theory and Karush-Kahn-Tucker (KKT) optimization conditions. Simultaneous solution of all MPECs forms an equilibrium problem with equilibrium constraints (EPEC). Solving the resulting EPEC using diagonalization algorithm and game theory, a market Nash equilibrium is obtained. Another goal is to solve the bi-level problem in a bi-objective way using the augmented epsilon constraint method, which maximizes the profit and minimizes the emissions of virtual power plant units. The proposed model is tested on a standard IEEE-24 bus system and the results indicate that, at the equilibrium point, the profit of a virtual power plant and GenCo will be less than in the initial state.

Abstract
In this study, the operation of an energy system composed of a battery energy storage system (BESS) and a conventional generator to compensate the forecasting error of renewable power production has been analyzed. A scenario with low forecasting error and another with high forecasting error have been synthetically modeled and incorporated to a computational model of the energy system. The results obtained from a case study suggest that a low forecasting error could be compensated by a single BESS. However, a high forecasting error would require the installation of a controllable power source such as a conventional generator.

Abstract
In this paper, multiarea economic dispatch (MAED) problems are solved by a novel straightforward process. The solved MAED problems include transmission losses, tie-line constraints, multiple fuels, valve-point effects, and prohibited operating zones in which small, medium, and large scale test systems are involved. The methodology of tackling the problems consists in a new hybrid combination of JAYA and TLBO algorithms simultaneously to take the advantages of both to solve even nonsmooth and non-convex MAED problems. In addition, a new and simple process is used to tackle with the interaction between areas. The objective is to economically supply demanded loads in all areas while satisfying all of the constraints. Indeed, by combining JAYA and TLBO algorithms, the convergence speed and the robustness have been improved. The computational results on small, medium, and large-scale test systems indicate the effectiveness of our proposed algorithm in terms of accuracy, robustness, and convergence speed. The obtained results of the proposed JAYA-TLBO algorithm are compared with those obtained from ten well-known algorithms. The results depict the capability of the proposed JAYA-TLBO based approach to provide a better solution.