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The reduction of global greenhouse gas emissions is one of the key steps towards sustainable development. The integration of Distributed Energy Resources (DERs) in power systems will help with emissions reduction. Virtual Power Plants (VPPs) can overcome barriers to participation of DERs in system operation. In this paper, a model is proposed for the energy management of a VPP including PhotoVoltaic (PV) modules, wind turbines, Electrical Energy Storage (EES) systems, Combined Heat and Power (CHP) units, and heat-only units. The multi-objective operational scheduling of DERs in the VPP focuses on maximizing the expected day-ahead profit of the VPP and minimizing the expected day-ahead emissions. The uncertainty of wind speed, solar radiation, market price, and electrical load is modeled using scenario based approach. Also, two-stage stochastic programming is implemented for modeling the VPP energy management. Three cases have been investigated for evaluating the proposed method: single-objective scheduling of VPP to maximize profit, single-objective scheduling of VPP to minimize emission and multi-objective economic/emission scheduling of VPP. The results indicate the appropriate economic and environmental performance of the proposed method, which provides the possibility of selecting a compromise solution for the VPP operator in accordance with environmental restrictions and economic constraints.

With the integration of distributed energy resources (DER), the traditional power systems have evolved toward modernized smart grids. Although smart grids open up the possibility for more reliable and secure energy management, they impose new challenges on real-time monitoring and control of the power grid. Fast, accurate, and robust SE is critical for monitoring cyber-enabled smart grids with high penetration of renewable energy resources. State estimation is a key function which plays a vital role in reliable system control. The focus of this paper is to give an overview about the smart grid state estimation (SGSE) and the applied learning-based methods in SGSE. This chapter investigate the challenges in the SGSE and a brief comparison between the transmission system state estimation (TSSE) and the SGSE. Moreover, it discussed the conventional methods and the filter-based model state estimators.

In this paper, the energy management of a microgrid including wind turbine, PhotoVoltaic (PV) modules, Combined Heat and Power (CHP) systems, fuel cells, power only units, heat only unit, Plug-in Electric Vehicles (PEVs), and thermal energy storage resources for supplying electrical and thermal loads is presented. For achieving a better management on demand side, both price-based and incentive-based Demand Response Programs (DRPs) have been used and their impacts on reducing the operational cost of microgrid in both grid-connected and island modes have been investigated. Also, the uncertainty of price, load, wind speed and solar radiation are taken into account in order to obtain more realistic results. By discretization of Probability Distribution Function (PDF) of each uncertain parameter, a set of scenarios is generated. Then, using a scenario reduction method based on mixed-integer linear optimization, the set of reduced scenarios is obtained. Two-stage stochastic programming approach is used to minimize the operational cost in microgrid energy management. The proposed method for microgrid energy management has been evaluated in three modes: grid-connected, grid-connected with DRPs, and island mode with DRPs.

Demand response programs (DRPs) bring in various economic, technical, and environmental benefits to power system such as avoiding price spike by peak reduction, postponing costly network expansions as a result of improving load factor, and pollution reduction due to reduced energy consumption. Precise modeling of customer response to DRPs helps operators to determine the impact of DRPs on economic and technical aspects of the system. Consequently, this chapter aims to develop comprehensive modeling of DRPs. Mathematical formulations are derived based on customer’s behavior characteristics and specifications of DRPs. The linear and nonlinear demand functions as well as constant and flexible price elasticities are taken into account in the extracted mathematical models. Numerical studies are also conducted for investigating the impact of DRPs on load profile and technical and economic aspects. As regards market clearing-based DRPs, an economic approach is presented for determining the optimal bids of customer in electricity markets.

Network security can be defined as the act of protecting any given network against threats that may lead to the availability of the network to be compromised. Moreover, we can also add that unauthorized access or even misuse of network-accessible resources are issues that network security must address. Traditional detection techniques are inefficient when dealing with huge amounts of data because their analysis processes are complex and time-consuming. Hence, the use of tools and techniques provided to us through big data can assist in the analysis and storage of data in intrusion detection systems to help reduce both processing and training time. This document presents a review of the work related to network security via machine learning.

The smart grid is a complex Cyber-Physical System (CPS) which integrates distributed energy resources, includes different interaction between customers and utility, and facilitates the participation of customers, in the electricity market. Despite all the technical, environmental and economic benefits of the smart grid, it is vulnerable to cyber-attacks because of the two-way communications. Cyber-attacks on the smart grid result in erroneous decisions by the control center. The consequences of these decisions could be transmission congestion or even worse consequences like cascading failures which causes catastrophic blackouts. State estimation is an important part of smart grid operation and control that critical power system applications such as optimal power flow calculation and contingency analysis depend on it. Consequently, the security of the state estimator is vital for maintaining the reliable and safe operation of the smart grid. The state estimator receives various real-time measurement data with errors from the smart grid and determines the best estimation of system state variables. The growing size and complexity of the smart grid have made state estimation a computationally expensive slow process and Traditional state estimation resulting in a slow calculation by iteration are not efficient. Also, Bad Data Detection (BDD) procedure is also included in the process of state estimation in order to identify cyber-attacks and protect the system. However, existing BDD mechanisms are not capable of detecting new types of attacks, which are injected in carefully planned efforts. The application of machine learning techniques in state estimation is a promising solution to deal with these issues. Machine learning improves the performance of state estimation and it is a good alternative for BDD techniques.

Microgrids bring in various benefits to power systems such as improvement in reliability, security, efficiency and cost reduction. They are also capable of integrating Demand Response (DR). Therefore, precise modeling of DR is an important issue. In this paper, a composite DR model, based on price elasticity of demand and customer benefit function, is employed in the microgrid energy management. This model helps with more realistic and accurate modeling of DR by considering different groups of customers having different load profiles and energy use habituates. Regarding the energy management, two-stage stochastic programming is used for modeling the optimization problem. Various distributed energy resources along with uncertainties are also taken into account. Simulation results demonstrate that the proposed method helps with more accurate modeling of DR. It is also shown that the implementation of Time of Use (TOU) program resulted in about 10% decrease in peak demand, around 3% reduction in energy consumption and about 5% decrease in the total operational cost of the microgrid.

Microgrids are structures that improve the reliability, efficiency, cost and emission in power systems. This paper presents the multi-objective economic/emission energy management of a microgrid including wind turbine, photovoltaic (PV) modules, combined heat and power (CHP) systems, power-only units, fuel cells, plug-in electric vehicles (PEV), heat-only unit and responsive loads. A price-based demand response program (DRP) is implemented to achieve a better management on demand-side. Also, the uncertainties of renewable generations, market price and load are modeled and two-stage stochastic programming is employed for modeling the optimization problem. The proposed model is evaluated in three case studies: single-objective energy management to minimize cost, single-objective energy management to minimize emission and multi-objective economic/emission energy management of the microgrid. The e-constraint method is used to generate the Pareto optimal solutions in the third case. The results demonstrate how the microgrid resources are scheduled to reduce the cost and emission. Moreover, the emission and cost are decreased by about 10% and 6% respectively. Therefore, the multi-objective approach is presented for the selection of a compromise solution.