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Smart grid is a typical cyber-physical system, which presents the dependence of power system operations on cyber infrastructure for control, monitoring, and protection purposes. The rapid deployment of synchrophasor measurements units (PMUs) supporting wide area measurement system (WAMS) in the smart grid transmission system has opened opportunities to enhance the grid operations through the introduction of WAMS applications. However, the increased deployment of synchrophasor technologies increases the effective attack surface available to attackers and exposes WAMS applications. Such applications have strict and stringent delay requirements, e.g., end-to-end delay as well as delay variation between measurements from different PMUs. In this paper, we present a mathematical model for constructing forwarding trees for PMUs’ measurements which satisfy the end-to-end delay as well as the delay variation requirements of WAMS applications at data concentrators. We illustrate that simple shortest path routing will result in larger fraction of data drop and that our method will achieve better delivery rate. We also validate our method against delay attacks using real-time co-simulation.

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.

The contemporary problem of scheduling the recharge operations of electric vehicles (EVs) has gained a lot of research attention. This is particularly true given the governmental and industrial confidence in a bright future for EVs accompanied with the widespread installation of an enormous number of charging stations across the world. As such, this paper addresses the delay-optimal scheduling of charging EVs at several charging stations (CSs) each with multiple charging outlets. At first, a centralized optimization framework is formulated using an integer linear problem (ILP) that accounts for the delayed arrival of EVs to CSs and the randomness in the requested recharge time interval. Simulation results showed the efficacy of the ILP model when compared to naive as well as sophisticated scheduling heuristics. Next, motivated by the scalability issues of the ILP model, this paper then proposes a distributed game-theoretical approach where each EV communicates with its selected CS and iterates on modifying its strategy until all EVs converge to selecting an appropriate CS that minimizes their waiting times for receiving services. The distributed game-theoretical approach recorded promising results especially when compared to the well-known shortest job first scheduling algorithm. Further, unlike the other approaches, which normally are centralized and suited for offline scheduling, the game-based method is suited for online scheduling since it played at anytime a batch of EVs requests charging services. The running time of the game is remarkably small and outperforms all other heuristics and its convergence to Nash equilibrium is guaranteed after only small number of iterations.

The evolution in microgrid technologies as well as the integration of electric vehicles (EVs), energy storage systems (ESSs), and renewable energy sources will all play a significant role in balancing the planned generation of electricity and its real-time use. We propose a real-time decentralized demand-side management (RDCDSM) to adjust the real-time residential load to follow a preplanned day-ahead energy generation by the microgrid, based on predicted customers’ aggregate load. A deviation from the predicted demand at the time of consumption is assumed to result in additional cost or penalty inflicted on the deviated customers. To develop our system, we formulate a game with mixed strategy which in the first phase (i.e., prediction phase) allows each customer to process the day ahead raw predicted demand to reduce the anticipated electricity cost by generating a flattened curve for its forecasted future demand. Then, in the second stage (i.e., allocation phase), customers play another game with mixed strategy to mitigate the deviation between the instantaneous real-time consumption and the day-ahead predicted one. To achieve this, customers exploit renewable energy and ESSs and decide optimal strategies for their charging/discharging, taking into account their operational constraints. RDCDSM will help the microgrid operator to better deal with uncertainties in the system through better planning its day-ahead electricity generation and purchase, thus increasing the quality of power delivery to the customer. We evaluate the performance of our method against a centralized allocation and an existing decentralized EV charge control noncooperative game method both of which rely on a day ahead demand prediction without any refinement. We run simulations with various microgrid configurations, by varying the load and generated power, and compare the outcomes.

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.

The selection of workers for carrying out tasks in mobile crowdsourcing systems that maximizes Quality of Service (QoS) is a challenging problem due to their diverse task completion properties and profit demands. The existing works in the literature are limited either by merely considering the minimization of task completion time only or exploitation of colocated locations of the worker and task. In this paper, we have developed an optimization framework to make a trade-off in between the profit of workers and task completion time. The proposed framework considers locations related to task's service point, task's delivery point and current location of the worker. The results of performance studies depict that the proposed system offers competitive task delivery time as well as workers' profit.

The Fifth Generation (5G) era is bringing tremendous new network capabilities enabling diverse services belonging to different business verticals (i.e., manufacturing, automotive, etc.) and provided with top-notch Quality of Service (QoS) (i.e., ultra-low latency, ultra-reliability, etc.). Empowered by soft-warization technologies such as Network Function Virtualization (NFV), 5G networks are envisioned to be agile, sustainable and self-organized. NFV promotes the automated provisioning of Network Services (NSs) through processing their traffic by a chain of Virtual Network Functions (VNFs). As VNFs are shared between multiple NSs, a clear approach to map and schedule the carried traffic of these services is required. Hence, in this paper, we solve the Latency-Aware Service Schedule Orchestration problem (LASSO) that jointly addresses the mapping and scheduling of services to VNFs. We formulate the problem as a Mixed Integer Linear Program (MILP) and we present ENCHAIN, a novel game-theoretic approach exploiting a scalable solution for the LASSO problem while providing each NS the freedom to decide on its own mapping and scheduling solution.

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.

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.

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.