Abstract
The increased integration of Electric Vehicles (EVs) into the distribution network can create severe issues—especially when demand response programs and time-varying electricity prices are applied, EVs tend to charge during the off-peak time to minimize the electricity cost. Hence, another peak demand might be created, and other solutions are required. Many researchers tried to solve the problem; however, limitations exist because of the decentralized topology of the network. The system operator is not allowed to control the end-users’ load due to security and privacy issues. To overcome this situation, we propose a novel data-energy management algorithm on the transformer’s level that controls the power demand profiles of the householders and exchange energy between them without violating their privacy and security. Our method is compared to an existing one in the literature based on a decentralized control strategy. Simulations show that our approach has reduced the electricity cost of the end-users by 3%, increased the revenue of the system operator, and reduced techno-economic losses by 50% and 42%, respectively. Our strategy shows better performance even with a 100% penetration level of EVs on the network, in which it respects the network’s constraints and maintains the voltage within the recommended limits.
Highlights
Scenario 3: In this scenario, we tried to satisfy both end-users and the system operator, in which the end-user pays less than scenario 2, and at the same time, the system operator increases its revenue compared to scenario 1, which is due to the minimization of techno-economic losses on the transformer and the network
This paper presents a novel energy-data management algorithm for a smart transformer, in which the main goal is to propose power and energy soft-constraint limits to homes that should be respected
The novelty of the algorithm can be summarized by the following points: (i) It proposes a variable soft-constrained power profile limit for each home; (ii) It classifies and sorts homes according to their average energy demand from the lowest to the highest; (iii) It proposes decentralized demand response and incentive programs for each individual home; (iv) It allows the homes to share unused energy between them
Summary
To mitigate the impact of the high penetration level of Electric Vehicles (EVs) on the distribution systems, advanced optimization algorithms and control strategies were developed including, but not limited, to (i) Centralized [1] (ii) Hierarchical [2], (iii) Multi-Agent [3], and (iv) Decentralized [4]. Despite the usage of advanced optimization algorithms to obtain optimal scheduling of the loads at homes, the optimal solution may not participate in improving the total power demand profile on the Distribution Transformer (DT) and the Distribution Network (DN) This is due to the fact that most of the controllable loads, especially EVs, tend to consume much energy during low electricity prices; the DT capacities can be exceeded, and many issues might appear. The transformer calculates some parameters, such as, but not limited to, the total harmonic distortion and the transformer’s lifetime Both system operators and end-users are allowed to access the real-time data of the transformer; they can better use the data to improve energy management and increase the efficiency of the network [16]. To the best of our knowledge, energy management systems for homes on the distribution network level using SST and DT, that satisfy both the system operator and the consumers, have not been studied yet, and need further investigation
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