Mosaddek Hossain Kamal Tushar

Smart Microgrids: Optimal Joint Scheduling for Electric Vehicles and Home Appliances

The integration of renewable energy sources and electrical vehicles (EVs) into microgrids is becoming a popular green approach. To reduce greenhouse gas emissions, several incentives are given to use renewable energy sources and EVs. By using EVs as electricity storage and renewable energy sources as distributed generators (DGs), microgrids become more reliable, stable, and cost-effective. In this paper, we propose an optimal centralized scheduling method to jointly control the electricity consumption of home appliances and plug-in EVs as well as to discharge the latter ones when they have excess energy, thereby increasing the reliability and stability of microgrids and giving lower electricity prices to customers. We mathematically formulate the scheduling method as a mixed integer linear programming (MILP) problem and solve it to optimality. We compare the optimal solution to that obtained from a scheduling framework, where EVs do not have discharge capabilities, decentralized charge control using game theory and to a solution obtained from a naive scheduling framework.

Distributed Real-Time Electricity Allocation Mechanism for Large Residential Microgrid

To reduce greenhouse gas emissions, several incentives are given to use renewable energy sources (RES) and plug-in electrical vehicles (PEVs). By using PEVs for electricity storage and RES as distributed generators, microgrids (MG) become more reliable, stable, and cost-effective. However, the high intermittent nature of energy sources and unpredictable presence of PEV in the MG offers new technological challenges to the smart grid/MG energy management system (EMS). In this paper, we propose a new distributed real-time electricity allocation (DRTA) scheme for the smart grid/MG EMS whose objectives are to reduce the electricity bill of the residential customers, to increase the overall social benefit of smart MG/grid community, and to increase the energy efficiency and reliability of the MG to rely on locally generated electricity. We formulate the problem as a noncooperative game using mechanism design and solve it to optimality. The proposed DRTA scheme converges to an optimal Nash equilibrium and produces a near-optimal solution. We compare the proposed scheme with a centralized optimal electricity consumption scheme and to the solution obtained from unregulated (natural) scheduling framework.

Volt-VAR Control Through Joint Optimization of Capacitor Bank Switching, Renewable Energy, and Home Appliances

Today, the evolution of smart grid, electric vehicles (EVs) with voltage to the grid mode, and deployment of renewable energy sources (RESs) are bringing revolutionary changes to the existing electrical grid. Volt-VAR optimization (VVO) is a well-studied problem, for bringing solutions to reduce the losses and demand along the distribution lines. The current VVO, however, does not acknowledge the role of elastic and inelastic loads, EVs, and RESs to reduce the reactive power losses and hence the cost of generation. We propose a mathematical model Volt-VAR and CVR optimization (VVCO)/optimal energy consumption model (OECM) to solve the VVO problem by considering load shifting, EV as the storage and carrier of the energy, and use of RES. The VVCO/OECM not only reduces the reactive load but also flatten the load curve to reduce the uncertainty in the generation and to decrease the cost. The system also considers the efficiency of the electrical equipment to enhance the lifetime of the devices. We develop a non-cooperative game to solve the VVCO/OECM problem. To evaluate the performance, we simulate the VVCO/OECM model and compare with the existing VVO solution. We found that our method took almost a constant time to produce a solution of VVO regardless of the size of the network. The proposed method also outperform the existing VVO solution by reducing the generation cost and flatten the load and minimizes the uncertainty in the power generation. Results have shown that exploiting RES will reduce the voltage drop through reducing the injection of reactive power to the system.

Optimal Energy Management and Marginal-Cost Electricity Pricing in Microgrid Network

The evolution of smart microgrid and its demand–response characteristics not only will change the paradigms of the century-old electric grid, but also will shape the electricity market. In this new market scenario, once always energy consumers, now may act as sellers due to the excess of energy generated from newly deployed renewable energy generators. In this paper, we propose an optimization scheme to minimize the electricity price with a framework for optimal trading of energy between sellers and buyers of the microgrid network (MGN). The proposed scheme is capable of solving the optimal power allocation problem for an MGN in a polynomial time without modifying the actual marginal costs of a generator. Initially, we mathematically formulate the problem as nonlinear nonconvex and later decompose the problem to separate the optimal marginal-cost model from the electricity allocation model. Then, we develop a divide-and-conquer method to minimize the electricity price by jointly solving the optimal marginal-cost model and electricity allocation problems. To evaluate the performance of the solution method, we develop and simulate the model with various cost functions and compare it with a first come first serve electricity allocation method and distributed energy trading for multiple microgrids.

Optimal Tree Construction Model for Cyber-Attacks to Wide Area Measurement Systems

The rapid deployment of phasor measurements in smart grid transmission system has opened opportunities to utilize new applications and enhance the grid operations. Thus, the smart grid has become more dependent on communication and information technologies, such as wide area measurement systems (WAMSs). WAMS are used to collect real-time measurement from sensors across widely dispersed areas. Such systems will improve real-time monitoring and control; however, recent studies have pointed out that the use of WAMS introduces significant vulnerabilities to the smart grid that can be leveraged by attackers. Therefore, preventing or reducing the damage of cyber attacks is critical to the security of the smart grid. In this paper, we focus on the relation between cyber-attack propagation and IP multicast routing, which is an essential aspect to the collection of phasor measurement units (PMUs) measurements. To this extent, we formulate the problem as the construction of a multicast tree that minimizes the propagation of cyber-attacks, while satisfying real-time and capacity requirements. The proposed attack propagation multicast tree is evaluated using IEEE 14-bus, IEEE 24-bus, IEEE 39-bus, the New England 39-bus, and IEEE 57-bus test systems.

Volt-VAR optimization by using electric vehicle, renewable energy and residential load-shifting

The development of smart grid, electric vehicles (EV), and integration of renewable energy sources (RES) changes the existing power grid. Volt-VAR optimization (VVO) is a well-known problem to reduce the losses along the distribution lines. Current VVO does not endorse the role of elastic and inelastic loads, EVs, and RESs to decrease the reactive losses and hence the cost of generation. We propose a mathematical model VVCO/OECM to solve the VVO problem by considering load shifting, EV as storage, and use of RES. The system also admits the efficiency of the electrical equipment to enhance the life of the devices. We develop a non-cooperative game to solve the VVCO/OECM problem. To evaluate the performance, we simulate the VVCO/OECM model and compare with the existing VVO solution (VVCO). We found that our method took nearly a constant time to produce a solution of VVO despite the size of the network. It outperforms the existing VVO solution by reducing the generation cost and flatten the load.

Optimal electricity pricing in a microgrid network

The evolution of microgrid and its demand-response characteristics not only will change the paradigms of the century-old electric grid but also will shape the electricity market. In this new scenario, once always energy consumers, now may act as sellers due to the excess energy generation from the newly deployed distributed generators (DGs). In this paper, we propose a novel mathematical model and its solution methodology to minimize the overall electricity price. The model is nonlinear and nonconvex due to the nonlinear and nonconvex characteristics of the marginal costs. To solve the minimum electricity price model (MEPM) problem efficiently and optimally, we decompose the problem into two subproblems: overall marginal cost problem (OMCP), and minimum cost allocation problems. A solution of OMCP determines the boundary of the overall marginal costs, and the allocation problem maps the optimal amount of energy from sellers to buyers for minimum price. The MEPM solution uses a divide-and-conquer method that divides the overall marginal cost boundary (solution OMCP) into two and determines the allocations for minimum cost, interactively. We compare MEPM with a first come first serve pricing scheme.