Smart DC Microgrid

The objective of this chapter is to elucidate on microgrid technologies, a comparison of direct current (DC) microgrid technology and alternating current (AC) microgrid technology, the role of the information and communication technology, demand response programs, and the evolution of Industry 4.0 in detail. The microgrid is a cyber-physical system. ICT is used for computing control algorithms and sending control information to actuators for physical processes. In a cyber-physical system, the physical processes, which are governed by the laws of physics, are controlled by computers. The computers are used for computing or executing the algorithms (i.e., the control logic) and the result is sent to the actuators in the form of control signal for actual control. In a microgrid, a consumer can act as a producer also, which is termed as the prosumer. This chapter explains the maximum power point tracking algorithm, software-defined battery, the operation of parallel converters, the working of prosumer, the demand response program, communication technologies, and the (industrial) Internet of Things.

Stochastic Coordination of the Wind and Solar Energy Using Energy Storage System Based on Real Time Pricing

In this paper, Stochastic synchronization of the Wind and Solar Energy Using Energy Storage system based on real-time pricing in the Day Ahead-Market Along with taking advantage of the potential of Demand Response programming, has been analyzed. Since renewables energies, loads and prices are uncertain, and planning is based on real-time pricing, the optimal biding proposition considers the wind power, solar system, and energy storage system. Uncertainty is addressed to solve the bidding strategy in a day-ahead market for optimal wind and PV power and optimal charging for energy storage. Batteries are the most promising device to compensate for the fluctuations of wind and photovoltaic power plants to mitigate their uncertainty. In general, using MILP is a suitable approach to address uncertainty as long as a linear formulation is acceptable for modeling either with continuous variables or integer ones. By setting some scenarios to formulate market prices, imbalance of energy, wind and solar system, the uncertainty problems could be easily solved by MILP solver. The model created enables the retailer to realize the potentials of the demand response program and exploit high technical and economic advantages. To ensure fair prices, a set of regulating constraints is considered for sales prices imposed by the regulation committees. A model is presented to optimize the electricity trading strategy in the electricity market, considering the uncertainty in the wholesale market price and the demand level. The retailer considered in this paper is a distribution company that is the owner and operator of the networks and operates under real-time pricing regulations. To model demand response, the elasticity coefficient is used. The proposed solution is implemented on a standard 144-bus sample network using a nonlinear integer programming method. The presented method results provide helpful and valuable information based on the optimal method proposed by the retailers considering the Demand response program and real-time pricing (RTP) system.

An Incentive-Based Implementation of Demand Side Management in Power Systems

The growing demand for electricity runs counter to European-level goals, which include activities aimed at sustainable development and environmental protection. In this context, efficient consumption of electricity attracts much research interest nowadays. One environment friendly solution to meet increased demand lies in the deployment of Renewable Energy Sources (RES) in the network and in mobilizing the active participation of consumers in reducing the peak of demand, thus smoothing the overall load curve. This paper addresses the issue of efficient and economical use of electricity from the Demand Side Management (DSM) perspective and presents an implementation of a fully-parameterized and explicitly constrained incentive-based demand response program The program uses the Particle Swarm Optimization algorithm and demonstrates the potential advantages of integrating RES while supporting two-way communication between energy production and consumption and two-way power exchange between the main grid and the RES.