Power distribution networks, incorporating electric vehicles (EVs) and battery energy storage systems (BESSs), can provide valuable flexibility to the upstream grid. This article proposes a new mechanism for modeling and optimizing two-way flexibility exchange between the distribution system operator (DSO) and flexible loads, aiming to minimize the DSO’s total cost while satisfying the flexibility requests of the upstream market operator. The DSO first solicits the participation of flexible loads, including EVs and BESSs, which can either accept or reject the request. Considering the agreed state of charge of participating resources, a flexibility market is then formulated, incorporating the user contribution index and Karush–Kuhn–Tucker conditions. The proposed mechanism is tested under various load conditions, price tariffs, and EV penetration levels. The results demonstrate significant cost reductions for DSOs, as they purchase less energy from the upstream market operator compared to scenarios without flexibility management.

Coordination between electric vehicles (EVs) and battery systems (BSs) plays a pivotal role in enhancing the flexibility of the electricity grid by offering demand response and energy storage capabilities. This paper proposes a new method for EV-BS coordination to meet the expected flexible load (FL) in each hour of the day. The profit of the Distribution System Operator (DSO) is formulated as a mixed-integer linear programming optimization problem. Additionally, different tariff prices are used to account for the uncertainty of the flexibility price in the electricity market. According to the proposed approach, the flexibility direction is first determined by the DSO, which can be upward flexibility, downward flexibility, or no flexibility, depending on different load conditions. The electricity market is then utilized to provide the expected FLs based on the flexibility direction and the behavior of EVs and BSs, with the participation fee in the proposed program. Simulation results show that the proposed approach increases the DSOs’ profit by considering the coordination between EVs and BSs during non-flexibility hours.

The increasing adoption of electric vehicles (EVs) presents voltage stability challenges in low-voltage (LV) residential distribution networks. EV aggregators can mitigate these issues by coordinating EV charging and discharging while enhancing economic returns through participation in day-ahead market with ancillary services. This paper proposes a novel multi-objective optimization (MOO) approach to maximize the aggregator’s profit including revenues from energy arbitrage, reserve capacity, regulation services and battery degradation costs while simultaneously minimizing voltage deviations within the distribution network. An augmented epsilon-constraint (AUGMENCON) method is implemented to explore the optimal trade-offs between profitability and voltage stability. The implemented method outperforms the Non-dominated Sorting Genetic Algorithm II (NSGA-II) in producing a more non-dominated Pareto front. The methodology is validated on an IEEE 33-bus LV residential network using the MATPOWER toolbox in MATLAB 2024b, demonstrating the feasibility and effectiveness of balancing economic incentives with voltage regulation constraints.

This article studies optimal planning and operation of electric vehicle (EV) charging stations within power distribution networks, which is crucial due to the growing penetration of EVs and distributed energy resources. Traditional approaches for planning and operation can be suboptimal and lead to significant grid upgrade costs. To tackle this, we propose an integrated framework that jointly optimizes the location, sizing, and operation of battery energy storage systems and EV charging stations. Our multiobjective optimization model minimizes power losses, operational costs, and environmental impacts while maximizing system reliability. We use genetic algorithms and deep deterministic policy gradients to solve this problem, departing from conventional peak-demand-based designs and leveraging typical demand profiles for battery energy storage system sizing. Real-time controllers are incorporated to adjust charging rates dynamically, responding to real-time variations. The proposed framework, validated on realistic test networks, demonstrates improved efficiency and stability, supporting sustainable and resilient grid operations.

This article studies the second-order consensus problem in networked systems containing the so-called Byzantine misbehaving nodes when only an upper bound on either the local or the total number of misbehaving nodes is known. The existing results on this subject are limited to malicious/faulty model of misbehavior. Moreover, existing results consider consensus among normal nodes in only one of the two states, with the other state converging to either zero or a predefined value. In this article, a distributed control algorithm capable of withstanding both locally bounded and totally bounded Byzantine misbehavior is proposed. When employing the proposed algorithm, the normal nodes use a combination of the two relative state values obtained from their neighboring nodes to decide which neighbors should be ignored. By introducing an underlying virtual network, conditions on the robustness of the communication network topology for consensus on both states are established. Numerical simulation results are presented to illustrate the effectiveness of the proposed control algorithm.

In this article, a data-driven adaptive control (DDAC) technique is proposed for the automatic generation control (AGC) problem of an interconnected power grid subject to deception attack (DA). The emergence of the Internet of Things (IoT) and the advancement of communication technologies have provided an opportunity for power system operators and designers to compensate for the lack of an appropriate model using a huge amount of data. However, they have also caused security challenges in the grid due to malicious attackers. This article focuses on the attack to the control network, which carries the AGC signals between the secondary and local primary frequency controllers. Intentional modifications of AGC signals during an attack may result in frequency instability because of saturation in governor signals. To counteract such an attack, a DDAC is suggested for a multiarea power system in which the system model is dynamically updated using real-time input and output signals. The model includes the attacker’s behavior, thus empowering the control system to act against it. The stability of the proposed controller is proved using the Lyapunov stability theory when the DA causes input saturation. Simulation results show that it can successfully tolerate a class of DAs and keep the multiarea power grid stable.

Knowledge of the Laplacian eigenvalues of a network provides important insights into its structural features and dynamical behaviours. Node or link removal caused by possible outage events, such as mechanical and electrical failures or malicious attacks, significantly impacts the Laplacian spectra. This can also happen due to intentional node removal against which, increasing the algebraic connectivity is desired. In this article, an analytical metric is proposed to measure the effect of node removal on the Laplacian eigenvalues of the network. The metric is formulated based on the local multiplicity of each eigenvalue at each node, so that the effect of node removal on any particular eigenvalues can be approximated using only one single eigen-decomposition of the Laplacian matrix. The metric is applicable to undirected networks as well as strongly-connected directed ones. It also provides a reliable approximation for the “Laplacian energy” of a network. The performance of the metric is evaluated for several synthetic networks and also the American Western States power grid. Results show that this metric has a nearly perfect precision in correctly predicting the most central nodes, and significantly outperforms other comparable heuristic methods.

In this paper, control of Battery Storage Systems (BSS) in power distribution grids with residential consumers as well as prosumers equipped with rooftop photovoltaic (PV) solar panels and Electric Vehicles (EV) is addressed. Different features of these Distributed Energy Resources (DERs), such as intermittent behaviour and the difference between the maximum generation time and the maximum demand, have caused several issues for electricity distributors in delivering high quality power. Smart control and scheduling of ESS and EVs is a promising approach to protect the grid against extra power injection from prosumers during day times while the benefit of household owners from DERs are still achieved. In this context, the performance of model-based controllers such as model predictive controllers (MPC) is compared with model-free data driven controllers (DDC) considering different complex scenarios that may happen in a distribution grid. The control objective is to minimize the difference between the net power exchanged with the main grid from the estimated average net load of prosumers. Our study on the real consumption data of about 40 residential consumers/prosumers in Victoria, Australia, demonstrates the strength of data-driven control approaches to deal with the complex environment of power distribution grids in the presence of DERs.

In this paper, we propose a simple cohesive framework to find an optimal directed control network topology with minimum number of links while a prescribed decay rate is satisfied in the transient response of a distributed control system. In order to guarantee the system's decay rate to be faster than a prespecified value, a constraint on the dominant eigenvalue of the system is required to be considered. This results in a nonconvex optimization problem as eigenvalue of a parametric nonsymmetric matrix is a nonconvex, nonsmooth, and even non-Lipschitz function. Here, we present a convex equivalent optimization problem whose minimizer also solves this eigenvalue optimization problem. This optimization problem proposes a state-feedback matrix which results in a decay rate faster than a given value while input signal costs are considered. The equivalent optimization problem in combination with sparsity-promoting optimal control constitutes a combinatorial optimization problem. Using alternating direction method of multipliers, the problem is decomposed into a chain of analytically solvable subproblems which are differentiable and separable. The proposed optimization framework includes relative preference between the topology of the control network and the decay rate of the system. The simulation results show the effectiveness of the proposed framework.

This paper addresses the problem of finding the most synchrony vulnerable node in complex networks, i.e. the node which removal has the maximum influence on synchronizability of the network. In large-scale networks, brute search techniques are often not computationally cost effective in identifying the most vulnerable node(s). Here, considering the eigenratio of the Laplacian matrix of a graph as the synchronizability metric, we propose a measure in order to approximately rank nodes based on their impact on the synchronizability. This metric is cost effective since it needs a single eigen-decomposition of the Laplacian matrix of the connection graph. Simulation results show that the proposed metric is accurate enough in predicting the most vulnerable node in synthetic networks with scale-free, Watts-Strogatz and Erdös-Rényi structures.

In this paper, the stability problem of formation of multi-agents subject to failures in their communication links is addressed. The objective of the formation control problem is to maintain the inter-agent distances to be constants over time using a distributed control algorithm implemented in each agent. Previous research results showed that a distributed gradient-based control can locally asymptotically stabilize an undirected formation. However, in the case of failures in the inter-agent communication network, the degrees of freedom for some nodes might become uncontrollable and, consequently, the formation starts deviating from the desired conditions due to uncertainties and noise. In this paper, it is proved that in a faulty formation system, if there still exists a path between the agents on the both sides of the failed link, the gradient-based control signal can recover the formation without adding any new link to the network. Based on this feature, an algorithm for recovering the formation from the fault is developed. Simulation results show that the proposed recovery algorithm can tolerate small values of delays in data communications.

In this paper, we propose an optimization methodology to find the optimal topology for the data communication network in distributed frequency control of power system. In order to implement a distributed cooperative control scheme, local controllers often share their data over a data communication network. Structure of this network has a major role in determining stability of secondary cooperative frequency control of a microgrid; and thus the structure can be optimized to have the best performance. Although distributed control signals may be delayed or dropped during their transmission, confident margin of the stability can reduce side effects of these inherent problems and makes power system more reliable. In this situation, the challenge is to find the best topology of data communication network giving the highest margin of the system stability. In this paper we define the problem of finding the best topology as an optimization problem. An eigenvalue perturbation analysis approach is used to approximate sensitivity of the stability performance of secondary cooperative control to adding/removing data communication links. Then, the optimization problem is solved using a simulated annealing optimization strategy. Our numerical simulations on sample networks show that the proposed rewiring-based optimization can successfully find the network structure with (near)-optimal stabilizability performance.

In this paper we study stability of a power system consisting distributed generation units. A complete model for the power grid including dynamics of loads as well as interchange of power is taken into account. A cooperative secondary control scheme takes into account internal dynamic of each node and is implemented to improve the stability of the system. In this paper, a condition for the stability of power grid in the presence of communication network is achieved. We support the results by numerical simulations on various connection topologies including scale-free and random structures. We find that increasing the number of communication links does not generally improve the stability of the whole power network.

Networked control systems (NCS) have gained an increasing attention in recent years due to their flexibility and cost reduction. The two main problems affecting stability and performance of closed-loop NCS are packet delay and drop. The problem of delay is severe in large scale NCS where the sensor-controller-actuator are connected via a network with large number of switches and routers. In this paper, we propose a real time optimization algorithm, called asexual reproduction optimization (ARO) which is inspired by asexual reproduction, to find the least delay link in the control loop. Simulation results reveal that the proposed algorithm remarkably outperforms the recently cited genetic algorithm (GA) in finding the minimum delay link.