Mahmood Joorabian

In this paper, a hierarchical method for the aggregation, control, and operation of thermostatically controlled loads (TCLs) within a virtual power plant (VPP) is presented. The goal is to integrate TCLs alongside wind turbines and photovoltaic resources within the VPP structure. The optimisation problem's objective function aims to maximise the VPP's profit in the day‐ahead (DA) and balancing markets. In the first stage of the model, distribution network reconfiguration (DNR) is employed to determine the optimal status of switches in the VPP topology and to obtain the offering power strategy for the DA market. The impact of DNR on the VPP's offering power strategy is also assessed. The control of the TCLs is performed using the privacy‐preserving proportional signals in the second stage to enable the VPP's active participation in the balancing market. Simulation results demonstrate that, compared with the case without network reconfiguration, the proposed framework increases the total VPP revenue by 11.03%, 13.63%, and 6.82% under low‐, medium‐, and high‐risk operating conditions, respectively, while reducing the balancing market power‐tracking error by 58.3%.

In this article, the planning and energy administration of energy hubs in electric, thermal and gas networks are presented, considering the resilience of the system against natural phenomena like floods and earthquakes. Each hub consists of bio-waste, wind and solar renewable units. These include non-renewable units such as boilers and combined heat and power (CHP) units. Compressed air and thermal energy storage are used in each hub. The design is formed as a bi-level optimization framework. In the upper level of the scheme, the energy management of networks bound to system resiliency is provided. This considers the minimization of annual operating and resilience costs based on optimal power flow equations in networks. In the lower-level model, the planning (placement and sizing) of hubs is considered. This minimizes the total building and operation costs of hubs based on the operation-planning equations for power supplies and storages. Scenario-based stochastic optimization models are used to determine the uncertainties of demand, the power of renewable systems, energy price and the accessibility of distribution networks’ elements against natural disasters. In this study, the Karush–Kuhn–Tucker technique is used to extract the single-level formulation. A numerical report for case studies verifies the potential of the plan to enhance the economic, operation and resilience status of networks with energy administration and the optimal planning of hubs in the mentioned networks. By determining the optimal capacity for resources and storage in the hubs located in the optimal places and the optimal energy administration of the hubs, the economic, exploitation and resilience situation of the networks are improved by about 27.1%, 97.7% and 23–50%, respectively, compared to load flow studies.

Renewable energy sources must be scheduled to manage power flow and load demand. Photovoltaic power generation is usually connected to power distribution networks and is not designed to add significant amounts of production in the event of increased electricity demand. Therefore, it is necessary to increase the generated capacity (i.e., hosting capacity) to meet the expansion in demand. This paper discussed two topics; the first is how to create an energy management strategy (EMS) for a hybrid micro-grid containing photovoltaic (PV) and battery energy storage system (BESS). A model was created within the MATLAB program through which the charging and discharging process of the BESS was managed, and the energy source was through PV. The model is connected to the leading network, where the m.file is linked to the model to control variable settings. This was carried out by using a logical–numerical modeling method. The second topic discussed how to evaluate hosting capacity (HC) without causing the network to collapse. This was achieved by choosing the best location and size for the PV. This study relied on the use of two algorithms, particle swarm optimization (PSO) and Harris hawks optimization (HHO). The fast decoupled load Flow (FDPF) method was adopted in the network analysis and finally the results of the two algorithms were compared.

A new power management model is presented here to prevent excessive frequency deviations by the more commitment of higher inertia power plants and more contribution of renewable resources or energy storage systems fast inertia response. To have a mixed-integer linear programming model, the primary frequency response constraints are linearized. Using distributionally robust optimization to model uncertainty of renewable sources in the primary frequency response strategy, considering technical limitation of network, and considering suitable case studies to investigate the capability of the proposed scheme are contributions of this paper. Model is examined on a real isolated microgrid. Results show that by activation of distributed energy resources the power management can be done with lower cost. Energy not supplied of microgrid can be reduced when energy storage systems are utilized as energy buffers in network. Comparing robust model with deterministic method shows the more expensive management procedure, however, a more frequency stability is obtained in the contingency condition. The proposed plan with the presence of only frequency control leads to a 361% increase in planning cost. However, if renewable resources are added to this scheme, a 62% reduction in planning cost occurs.

This study deals with the operational control strategy of a Hybrid System (HS) for a residential home equipped with a Geothermal System (GS). HS includes photovoltaic (PV), wind turbine (WT), battery energy storage system (BESS), and diesel generator (DG). The thermodynamic module of the GS was used to calculate the value of the electrical energy that could be saved, and the variation in environmental temperature during the day was considered. The presented model was studied using different scenarios for a residential home in Iraq and analyzed in terms of economic and environmental aspects. The results show that during some hours of the day, a GS minimizes the power drawn from the DG, reducing the cost and emissions of the total electrical power generation.

This paper presents the islanding and peninsulating of distributed generators (DGs), such as wind and solar ‎power plants, that feed microgrid systems. However, the paper does not focus on just an ordinary microgrid but ‎large microgrids that have several sub-microgrids with renewable energy resources and multiple connection ‎points (MCPs) to different grids. When islanding happens, the main microgrid disconnects some connection ‎points from grids whereas some connection points to other grids could be connected and divided into some sub-‎microgrids for better stability. Two new definitions are proposed for large microgrid islanding: percentage of ‎islanding and peninsulating. The former means how much it is possible that an islanding happens before it ‎happens, and the latter means that: “after separating from some connection points to grids in a large microgrid ‎with MCPs to different grids, remained large microgrid network is an island or a peninsula that is connected in ‎some connection points to other grids? So, peninsulating a large microgrid depends on the number of connection ‎points, at least two points, to different grids. This paper describes these two new definitions. The method involves ‎the measurement of utility currents, voltages, and other signals through a bidirectional communications system in ‎smart grids. These signals are used to calculate the percentage of islanding and decide on microgrid islanding or ‎peninsulating.

Microgrids (MGs), which can incorporate renewable energies such as wind and solar, can be divided into several sub-MGs with multiple connection points (MCPs) to the grids. However, this configuration is not ideal for MG operation due to the lack of adequate protection and operation mechanisms that ensure the safe and reliable functioning of distributed generation. A key issue with these MGs is the identification of islanding, which is challenging due to the presence of a broad non-detection zone (NDZ). Passive islanding identification approaches primarily depend on over/under voltage protection (OVP/UVP), over/under frequency protection (OFP/UFP), and monitoring metrics, such as phase jump at the point of common coupling (PCC). This study examines the power equations for real and reactive power in renewable-energy-based MGs (referred to as renewable MGs) with multiple connections to different grids and MGs, which are of significant size. The analysis focuses on the NDZ of OVP/UVP and OFP/UFP approaches. Passive approaches observe the changing system parameters that occur when the MG is isolated, while active methods depend on the system's reaction to a minor disturbance introduced to identify the isolation situation. Traditional passive islanding detection approaches exhibit a significant NDZ that may compromise the accuracy of islanding detection in these types of MGs. Even if one grid is disconnected, the MG remains connected to other grids, preventing islanding. Consequently, typical active islanding detection methods are unable to identify the off-grid status.

This paper presents the optimal scheduling of active distribution network (ADN) with linear and non‐linear loads in the presence of an integrated unit of electric spring and electric vehicle parking lot (IUEE). The proposed scheme is formulated in the form of three‐objective optimization, which minimizes the expected operating cost of the ADN, voltage deviation function (VDF), and the total harmonic distortion (THD) of the voltage in separate objective functions. Constraints include harmonic power flow (HPA) equations, operation and power quality limits, and operation model of the IUEE. This scheme has uncertainties of load, energy price, and energy demand of EVs, which are modelled here using stochastic programming. Next, the sum of the weighted functions method as a subset of the Pareto optimization technique is adopted to extract the integrated single‐objective problem. Then, Sine‐Cosine Combined Algorithm (SCA) and Gray Wolf Optimization (GWO) are used to achieve an optimal solution. In the end, by examining the numerical results obtained from the implementation of the proposed scheme on a 69‐bus ADN, the capability of the design in enhancing the economic situation, operation, and power quality of the ADN compared to network power flow studies obtained by optimal scheduling of the IUEE is confirmed.