Mohammad Javadi

Abstract
This chapter proposes a day-ahead scheduling framework in an energy hub (EH), integrating different energy conversion and storage technologies to efficaciously fulfill various types of load demands. The mentioned EH is capable of synchronously managing electrical, cooling, and heat load demands. The system is equipped with a combined heat and power (CHP) generating unit that efficiently supplies both heat and electricity. Furthermore, there are an electric heat pump and a boiler that also supply the heating load, while the heater is specifically employed for direct heating usage. The system includes an absorption chiller to supply a cooling load. This chiller absorbs waste heat from the CHP unit, resulting in improved energy efficiency. Battery storage systems enable the efficient use of energy by storing surplus power during times of low demand for future consumption. In addition, solar photovoltaic panels are included to capture renewable energy, therefore decreasing reliance on traditional energy sources and mitigating environmental consequences. The EH also includes a saltwater desalination technology operating together with the energy network to ensure the supply of freshwater, which is especially vital in dry areas. The desalination process is fueled by both renewable and produced thermal energy, thus maximizing resource use and reducing operating costs. The presented scheduling model has been formulated within a mixed-integer linear programming framework, implemented in GAMS, and solved by using the CPLEX solver to ensure optimal operation and minimum computational burden. This chapter provides a broad guideline of how the integrated systems operate. © 2025 Elsevier B.V., All rights reserved.

Abstract
This paper presents a fast and accurate optimization technique for optimal power flow (OPF) that can be conveniently applied to transmission and distribution systems. The method is based on the branch flow and DC optimal power flow (DCOPF) models. As the branch flow model is independent of the bus voltage angle, the model needs further development to enable use in meshed transmission systems. Thus, this paper adds the bus voltage angle constraint as a key constraint to the branch flow model so that the voltage angle can also be used in the power flow model in addition to the voltage magnitude control. The problem is based on second-order programming and modeled as a quadratically-constrained programming (QCP) problem solved using the CPLEX solver in GAMS. The functionality of the proposed model is tested utilizing four standard distribution systems, three transmission systems, a combined transmission-distribution network. The studied distribution systems include the 33-bus, 69-bus, 118-bus distribution (118-D) test systems, and 730-bus distribution system (730-D). Additionally, the studied transmission systems include 9-bus, 30-bus, and 118-bus transmission (118-T) test systems. The combined transmission-distribution system included the 9-bus transmission system with three connected distribution systems. The simulation results obtained from the developed technique are compared to those obtained from a conventional optimal flow model. The power losses and the absolute error of the solution are used as the two metrics to compare the methods' performance for distribution networks. The absolute error of the solution derived from the proposed hybrid OPF compared to MATPOWER for the 33-bus system is 0.00198 %. For the 69-bus system, the error is 0.00044 %. In addition, for the 118-D and 730-D systems, the absolute errors are 0.0026 %, and 0.05 %, respectively. For the transmission network, the operating costs and the solution absolute error are the two metrics used for comparing the proposed hybrid OPF model and MATPOWER. The results indicate the superior performance of the hybrid OPF model to the Newton-Raphson method in MATPOWER in terms of operating cost. In this regard, cost reductions relative to values given by MATPOWER are 0.0005 %, 0.838 %, and 0.015 %, for the 9-bus, 30-bus, and 118-T systems, respectively. The simulation studies demonstrate the performance of the presented branch flow-based model in solving the OPF problem with accurate results.

Abstract
The increasing penetration of electric vehicles will be accompanied for a wide deployment of charging infrastructures. Large charging demand brings formidable challenges to existing power networks, driving them near to their operational limits. In this regard, it becomes pivotal developing novel energy management strategies for active distribution networks that take into account the strategic behaviour of parking lots. This paper focuses on this issue, developing a novel energy management tool for distribution networks encompassing distributed generators and parking lots. The new proposal casts as a tri-level game equilibrium framework where the profit maximization of lots is implicitly considered, thus ensuring that network-level decisions do not detract the profit of parking owners. The original tri-level model is reduced into a tractable single-level mixed-integer-linear programming by combining equivalent primal-dual and first-order optimality conditions of the distribution network and parking operational models. This way, the model can be solved using off-the-shelf solvers, with superiority against other approaches like metaheuristics. The developed model is validated in well-known 33-, and 85-bus radial distribution systems. Results show that, even under unfavourable conditions with limited distributed generation, charging demand is maximized, thus preserving the interests of parking owners. Moreover, the model is further validated through a number of simulations, showing its effectiveness. Finally, it is demonstrated that the developed tool scales well with the size of the system, easing its implementation in real-life applications.

Abstract
The increasing occurrence of extreme weather events has severely compromised the resilience of power distribution systems, resulting in widespread outages and substantial economic losses. This paper proposes a novel solution to enhance the resilience of distribution networks without the need for significant infrastructure upgrades. We introduce a bilevel optimization framework that integrates Demand Response Programs (DRPs) to strategically manage electricity consumption and mitigate the impact of system disruptions. The approach fosters collaboration between Distribution System Operators (DSOs) and Demand Response Aggregators (DRAs), optimizing both operational resilience and economic efficiency. To solve the bilevel problem, we employ a Mathematical Program with Equilibrium Constraints (MPEC), transforming the bilevel model into a single- level problem by utilizing the Karush-Kuhn-Tucker (KKT) conditions. This method is applicable when the lower-level problem is convex with linear constraints. The model also incorporates Long Short-Term Memory (LSTM) neural networks for wind generation forecasting, enhancing decision-making precision. Furthermore, we conduct multiple case studies under varying severities of incidents to evaluate the method's effectiveness. Simulations performed on the IEEE 33-bus test system using GAMS and Python validate that the proposed method not only improves system resilience but also encourages active consumer participation, making it a robust solution for modern smart grid applications. The simulation results show that by performing DRP to handle the contingencies in a high-impact incident, the resilience of the system can be improved by 5.3%.

Abstract