• Energy Research

Abstract This paper addresses optimal day-ahead scheduling of Combined Heat and Power (CHP) units of an Active Distribution Network (ADN) with Electric Storage Systems (ESS) and Thermal Storage Systems (TSS) considering Industrial Customers (ICs) inter-zonal power exchanges. The ADN operator may use CHP units to supply its ICs and based on smart grid conceptual model, it can transact electricity with upward wholesale electricity market and its downward ICs' systems; meanwhile its ICs can transact energy with each other through the ADN main grid. Basically, the optimal scheduling of CHP units problem is a Mixed Integer Non Linear Programing (MINLP) problem with many stochastic and deterministic variables. However, the electricity transactions between the ADN and its ICs in normal and contingency scenarios may highly complicate this problem. In this paper, linearization techniques are adopted to linearize equations and a two-stage Stochastic Mixed-Integer Linear Programming (SMILP) model is utilized to solve the problem. The first stage models behavior of operation parameters and minimizes the operation costs; check the feasibility of the ICs' requested firm and non-firm power exchanges, and the second stage considers the system's stochastic contingency scenarios. The competitiveness of ADN in the deregulated market can be improved by adjustment of the proposed decision variables in the two stage optimization procedure. The proposed method is applied to 18-bus, 33-bus IEEE test systems. The effectiveness of the proposed algorithm has been investigated.

Abstract Short-term load and price forecasting is an important issue in the optimal operation of restructured electric utilities. This paper presents a new intelligent hybrid three-stage model for simultaneous load and price forecasting. The proposed algorithm uses wavelet and Kalman machines for the first stage load and price forecasting. Each of the load and price data is decomposed into different frequency components, and Kalman machine is used to forecast each frequency components of load and price data. Then a Kohonen Self Organizing Map (SOM) finds similar days of load frequency components and feeds them into the second stage forecasting machine. In addition, mutual information based feature selection is used to find the relevant price data and rank them based on their relevance. The second stage uses Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) for forecasting of load and price frequency components, respectively. The third stage machine uses the second stage outputs and feeds them into its MLP-ANN and ANFIS machines to improve the load and price forecasting accuracy. The proposed three-stage algorithm is applied to Nordpool and mainland Spain power markets. The obtained results are compared with the recent load and price forecast algorithms, and showed that the three-stage algorithm presents a better performance for day-ahead electricity market load and price forecasting.

Abstract Multi‐year generation and transmission expansion planning (MY‐G&TEP) is a critical issue in power systems. The present paper considers the optimal placement of Fixed Series Compensation (FSC) and Phase Shifting Transformer (PST) proposes a pool‐market‐based mathematical model (MY‐G&TEP) for maximizing social welfare (SW) and reducing investment costs of transmission lines and new power plants. Following the determination of optimal strategy, the present paper compares the usefulness of PSTs and FSCs in the MY‐G&TEP problem in three scenarios. Since MY‐G&TEP is a complex hybrid, mixed‐integer linear programming (MILP), and nonlinear optimization problem, the YALMIP toolbox and CPLEX solver have been applied to find the optimal solution, a globally optimized solution is obtained. For evaluation, the proposed model has been tested on the IEEE 24‐bus and IEEE 57‐bus systems, and the simulation results indicate that the installation of PST and FSC not only improves market conditions but also increases the flexibility of MY‐G&TEP, and adding these FACTS devices to the studied system leads to an increase in the network's performance and enhancement of objectives of the proposed model.

Abstract This paper addresses an integrated framework for the dynamic capacity withholding assessment of an independent system operator that determines the mid-term maintenance scheduling of generation companies and day-ahead scheduling of wholesale market participants. The main contribution of this research is that two dynamic capacity-withholding indices are proposed for mid-term and day-ahead scheduling of generation companies that estimate the dynamic capacity withholding opportunities of generation units in an ex-ante manner. The proposed framework is another contribution of this research that uses a four-stage optimization process that the system operator can detect and prevent the formation of withholding groups. The optimal maintenance scheduling from the generation companies viewpoint is assessed in the first-stage problem that considers different mid-term withholding opportunities. The optimal mid-term maintenance scheduling is carried out in the second-stage problem that recognizes and rejects the dynamic capacity withholding of generation companies. The optimal scheduling of day-ahead generation companies considering their dynamic capacity withholding is the third contribution of this paper that optimizes the scheduling of generation units for day-ahead horizon considering responsive loads. The proposed method is applied to 30-bus, 57-bus and 118-bus IEEE test systems. A full competition algorithm is also carried out to evaluate the competition states of generation companies. The proposed algorithm detected that the dynamic capacity withholding might lead to increase of nodal price by about 279.22%, 764.43%, and 851.2% for 30-bus, 57-bus, and 118-bus IEEE test systems with respect to the non-capacity withholding conditions, respectively.

Abstract This paper presents a two-level optimization problem for optimal day-ahead scheduling of an active distribution system that utilizes renewable energy sources, distributed generation units, electric vehicles, and energy storage units and sells its surplus electricity to the upward electricity market. The active distribution system transacts electricity with multiple downward energy hubs that are equipped with combined cooling, heating, and power facilities. Each energy hub operator optimizes its day-ahead scheduling problem and submits its bid/offer to the upward distribution system operator. Afterwards, the distribution system operator explores the energy hub’s bids/offers and optimizes the scheduling of its system energy resources for the day-ahead market. Further, he/she utilizes a demand response program alternative such as time-of-use and direct load control programs for downward energy hubs. In order to demonstrate the preference of the proposed method, the standard IEEE 33-bus test system is used to model the distribution system, and multiple energy hubs are used to model the energy hubs system. The proposed method increases the energy hubs electricity selling benefit about 185% with respect to the base case value; meanwhile, it reduces the distribution system operational costs about 82.2% with respect to the corresponding base case value.

The simultaneity of power systems development and uncertainty of system elements has promoted the importance of probabilistic load flow (PLF) in the operating and planning studies of the system. This clarifies that the use of the fast and accurate approaches for PLF computation is necessary. To achieve this objective, this study presents an analytical technique, based on the properties of Laplace transform (LT). The suggested methodology is applicable for every continuous probability distribution function as the input random variable. The proposed procedure is applied to the MATPOWER 9- and 118-bus test systems. To validate the combined cumulants and LT (CCLT) technique, the results are compared with the Monte Carlo simulation and the cumulants method combined with the maximum entropy (CCME) principle. The test results show that the proposed approach gives accurate results, with the lower computational burden comparing CCME. Furthermore, the method formulation and case study results demonstrate that the CCLT method is mathematically straightforward and computationally efficient.

This paper presents an integrated framework for the optimal resilient scheduling of an active distribution system in the day-ahead and real-time markets considering aggregators, parking lots, distributed energy resources, and Plug-in Hybrid Electric Vehicles (PHEVs) interactions. The main contribution of this paper is that the impacts of traffic patterns on the available dispatchable active power of PHEVs in day-ahead and real-time markets are explored. A two stage framework is considered. Each stage consists of a four-level optimization procedure that optimizes the scheduling problems of PHEVs, parking lots and distributed energy resources, aggregators, and active distribution system. The distribution system procures ramp-up and ramp-down services for the upward electricity market in a real-time horizon. The active distribution system can utilize a switching procedure to sectionalize its system into a multi-microgrid system to mitigate the impacts of external shocks. The model was assessed by the 123-bus test system. The proposed algorithm reduced the interruption and operating costs of the 123-bus test system by about 94.56% for the worst-case external shock. Further, the traffic pattern decreased the available ramp-up and ramp-down of parking lots by about 58.61% concerning the no-traffic case. ; © 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). ; fi=vertaisarvioitu|en=peerReviewed|

Abstract This paper presents a multi-stage day-ahead and real-time optimization algorithm for scheduling of system’s energy resources in the normal and external shock operational conditions. The main contribution of this paper is that the model considers the non-utility electricity generation facilities capacity withholding opportunities in the optimal scheduling of system resources. The real-time simulation of external shock impacts is another contribution of this paper that the algorithm simulates the sectionalizing of the system into multi-microgrids to increase the resiliency of the system. The optimization process is categorized into two stages that compromise normal and contingent operational conditions. Further, the normal operational scheduling problem is decomposed into three steps. At the first step, the optimal day-ahead scheduling of system resources and the switching of normally opened switches are determined. At the second step, the optimal real-time market scheduling is performed and the switching of normally closed switches is optimized. At the third step, different extreme shock scenarios are simulated in the real-time horizon and the effectiveness of sectionalizing the system into multi-micro grids are assessed. Finally, at the contingent operational conditions, the optimal topology of the system and scheduling of energy resources are determined. The proposed method was successfully assessed for the 33-bus and 123-bus test systems. The algorithm were reduced the expected cost of the worst-case contingencies for the 33-bus and 123-bus systems by about 97.89% and 88.11%, respectively. Further, the average and maximum values of the 123-bus system capacity-withholding index for real-time conditions reduced by about 67.40% and 71.05%, respectively.