Ehsan Heydarian-Forushani

This article presents an optimization‐based multi‐criteria decision‐making framework in order to determine the optimal amount of photovoltaic (PV) hosting capacity in the distribution network, taking into account technical and economic aspects. As an effective tool for facilitating PV integration, the demand response (DR) is also integrated into the model using the price‐elasticity concept. In this way, a comprehensive set of DR programs, including price‐based, incentive‐based and combinational programs, are modelled. In order to provide a guideline for distribution network operator to implement the most effective DR program, different programs are prioritized considering the defined economic and technical features using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The proposed model is tested on a 14‐bus IEEE test system, and the simulation results show that critical peak pricing programs increase the ability of the distribution network to integrate PV resources more than 18.4 kW compared to the general case study. Moreover, it reduces the daily energy losses by 227.7 kWh.

The building energy management system (BEMS) components, that is, the heating, ventilation, and air conditioning (HVAC) systems, refrigerators, and lighting significantly impact energy consumption. This paper presents the modelling, including HVAC and refrigerator with interior heat and external illuminus of lighting for residential buildings. The energy consumption of these components has been examined using eight meta‐heuristic algorithms with weather conditions of five cities in India. The statistical and box‐plot analysis results recommended the original artificial ecosystem algorithm compared to others for energy management. The results of the proposed smart residential building have been compared with the published model results of a residential building with and without BEMS and an economic analysis has also been carried out. The simulation results show that the maximum load has been shifted to off‐peak durations. The energy savings in the proposed BEMS compared to existing BEMS and without BEMS are 34% and 57%, respectively.

Electric vehicles (EVs) have significant potential to offer unused capacity in ancillary service markets, providing unique opportunities for market operators to utilise these resources. EVs have a rapid response and high availability, making them a good fit for the frequency containment reserve (FCR) market. However, EV aggregators (EVAGs) must aggregate capacity blocks due to the limited capacity of individual EVs. An application of a supervised machine learning method named XGBoost is suggested to help EVAGs predict the amount of EV participation in the FCR market. The objective is to forecast yearly involvement using data from only a single week, using the game theory method SHapley Additive exPlanations (SHAP) to minimise extra data. The proposed strategy helps aggregators and uses feature engineering to select EVs with high potential to boost revenue. The proposed framework is effective in predicting EV performance in the DK‐2 market, as shown by multiple analyses.

The hosting capacity of solar photovoltaics (PVs) in the distribution network can be impacted by the violation of power flow constraints such as power loss, voltage, and line loadings. In this paper, a planning structure based on optimal PV placement and optimal operation of the smart inverters and soft open point (SOP) is proposed in order to investigate the loss of power, voltage profiles, line loadings and total operation cost of the distribution system. The stochastic nature of the daily load and PV generation is considered in power flow optimization by the proposed method. Respective objective functions are optimized using a genetic algorithm (GA) according to probabilistic power flow. While the desired objective function is optimized, the optimality of the PV locations and contribution of smart inverters and SOP is ensured through GA optimization. The proposed model is investigated through numerical analyses on IEEE 33‐bus and IEEE 69‐bus distribution systems considering different cases of reactive power supports. Developed GA is able to deal with challenges of convergence that are imposed by complex nonlinear formulations. Obtained results prove the superiority of the developed program for the investigation of the PV hosting capacity in both technical and cost aspects.

This paper proposes an operational planning model based on optimal active and reactive power control strategies to enhance solar photovoltaics (PV)’ hosting capacity in distribution networks. Reactive power control is carried out through optimum static VAr compensators (SVCs) placement, while active power control is performed through flexible loads, particularly shiftable and interruptible loads. The first stage of the proposed two‐stage stochastic model assigns decision‐making regarding calculating PV hosting capacity at different nodes, in addition to the allocation and capacity of SVCs. In the second stage, the first stage decisions are assessed to ensure the power flow constraints under various uncertainties such as daily load and stochastic PV generation. The presented model is investigated through numerical analyses on modified IEEE 15‐bus and IEEE 33‐bus distribution systems considering different active‐reactive strategy cases. While most previous works only rely on one type of active or reactive power control strategy, this study investigates the challenges of the respective application of active and reactive power control in various modes of fundamental practices. The obtained results prove the superiority of the proposed hybrid active‐reactive control strategy for enhancing PV hosting capacity compared to respective active or reactive power controls.

In a restructured electricity market, a large consumer could supply its electricity consumption through its own available resources or purchasing from the electricity markets. Moreover, it could participate in demand response programs (DRPs) and also supply some portion of its demand from bilateral contracts. In this regard, the large consumer's objective is to minimize its own operation cost while facing several uncertainties that could affect its optimal decisions. The decision‐making problem becomes more difficult especially when the historical data is incomplete, or when the data is not available. This paper proposes an optimal decision‐making framework for a large consumer in the face of a comprehensive set of uncertain resources including electricity market price, internal electricity consumption, renewable power generation, and DR participation. To this end, a probabilistic‐possibilistic methodology is proposed to model the uncertainty of the electricity market prices in a more accurate way in comparison with existing approaches through the concept of Z‐number method. The uncertainty of internal electricity consumption is modelled by means of the seasonal ARIMA model, while the stochastic generation of its existing renewable resources are modelled through appropriate probability distribution functions. Moreover, the uncertainty of DRPs is considered by Markov chain. The large consumer also could sign bilateral contracts with generation companies to provide a portion of its required electricity demand. The simulation results confirm the effectiveness of the Z‐number methodology in comparison with other techniques for handling the uncertainties.