Day Ahead Demand Response Using Load Shifting Technique in Presence of Increased Renewable Penetration

Arun, C.; Aswinraj, R.; Bijoy, M. T.; Nidheesh, M.; Micky, R. Rohikaa

doi:10.1109/i2ct54291.2022.9825258

Cited by 7 publications

(4 citation statements)

References 22 publications

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“…The active and reactive power balance equations for each bus of radial networks are, respectively, represented by equality constraints (10) and (11). The voltage magnitude limits of the i-th bus are represented by constraint (12), while the current flow limits for each phase, I p,i,y , are represented by (13), with an upper limit of I p . Active and reactive…”

Section: Constraints Setmentioning

confidence: 99%

“…each phase, 𝐼 , , , are represented by (13), with an upper limit of 𝐼 . Active and reactive power of DGs must not exceed upper and lower boundaries as, respectively, given by constraints ( 14) and (15).…”

Section: Solution Techniquementioning

confidence: 99%

“…Several recent kinds of research have been carried out regarding the demand response [11][12][13] to minimize the load curve, considering the presence of DGs. The authors of [11] considered both demand-side management and network reconfiguration simultaneously.…”

Section: Introductionmentioning

confidence: 99%

“…To deal with the problem of photovoltaic and wind energy consumption and minimize the total cost of energy purchase, an optimal real-time dispatch model is established in [12], considering the grid security constraints and supply and demand constraints to ensure safe network operation. The authors of [13] addressed a priority-based load shifting to bridge the gap between demand and supply. The power consumer can then cooperate with the utility by managing its demand, that is, by scheduling flexible appliances based on consumption priority.…”

Section: Introductionmentioning

confidence: 99%

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Capacity Management in Smart Grids Using Greedy Randomized Adaptive Search Procedure and Tabu Search

2023

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Over time, distribution systems have progressed from small-scale systems to complex networks, requiring modernization to adapt to these increasing levels of active loads and devices. It is essential to manage the capacity of distribution networks to support all these new technologies. This work, therefore, presents a method for evaluating the impact of optimal allocation and sizing of DGs and load shedding for response demand programs on distribution networks to improve the reliability and financial performance of electric power systems. The proposed optimization tool uses the Greedy Randomized Adaptive Search Procedure and Tabu Search algorithms. The combined optimization of DG allocation simultaneously with load shedding, reliability indices, load transference, and the possibility of islanded operation significantly improves the quality of the planning proposals obtained by the developed method. The results demonstrate the efficiency and robustness of the proposed method, improving the voltage profile by up to 2.02%, relieving the network capacity, and increasing the load restoration capability and reliability. Statistical analysis is also carried out to highlight the performance of the proposed methodology.

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Section: Constraints Setmentioning

confidence: 99%

Section: Solution Techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Capacity Management in Smart Grids Using Greedy Randomized Adaptive Search Procedure and Tabu Search

2023

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Optimization of a Cluster-Based Energy Management System Using Deep Reinforcement Learning Without Affecting Prosumer Comfort: V2X Technologies and Peer-to-Peer Energy Trading

Yavuz,

Kivanç

2024

IEEE Access

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The concept of Prosumer has enabled consumers to actively participate in Peer-to-Peer (P2P) energy trading, particularly as Renewable Energy Source (RES)s and Electric Vehicle (EV)s have become more accessible and cost-effective. In addition to the P2P energy trading, prosumers benefit from the relatively high energy capacity of EVs through the integration of Vehicle-to-X (V2X) technologies, such as Vehicle-to-Home (V2H), Vehicle-to-Load (V2L), and Vehicle-to-Grid (V2G). Optimization of an Energy Management System (EMS) is required to allocate the required energy efficiently within the cluster, due to the complex pricing and energy exchange mechanism of P2P energy trading and multiple EVs with V2X technologies. In this paper, Deep Reinforcement Learning (DRL) based EMS optimization method is proposed to optimize the pricing and energy exchanging mechanisms of the P2P energy trading without affecting the comfort of prosumers. The proposed EMS is applied to a small-scale cluster-based environment, including multiple (6) prosumers, P2P energy trading with novel hybrid pricing and energy exchanging mechanisms, and V2X technologies (V2H, V2L, and V2G) to reduce the overall energy costs and increase the Self-Sufficiency Ratio (SSR)s. Multi Double Deep Q-Network (DDQN) agents based DRL algorithm is implemented and the environment is formulated as a Markov Decision Process (MDP) to optimize the decision-making process. Numerical results show that the proposed EMS reduces the overall energy costs by 19.18%, increases the SSRs by 9.39%, and achieves an overall 65.87% SSR. Additionally, numerical results indicates that model-free DRL, such as DDQN agent based Deep Q-Network (DQN) Reinforcement Learning (RL) algorithm, promise to eliminate the energy management complexities with multiple uncertainties.INDEX TERMS Energy management system, peer-to-peer energy trading, vehicle-to-home, multi-agent reinforcement learning, deep reinforcement learning, smart grids.

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Potential Evaluation Method for Aggregated Demand Response Resources Based on User Pattern Recognition

Chen

Zhang

Chen

et al. 2023

2023 6th International Conference on Energy, Electrical and Power Engineering (CEEPE)

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Day Ahead Demand Response Using Load Shifting Technique in Presence of Increased Renewable Penetration

Cited by 7 publications

References 22 publications

Capacity Management in Smart Grids Using Greedy Randomized Adaptive Search Procedure and Tabu Search

Capacity Management in Smart Grids Using Greedy Randomized Adaptive Search Procedure and Tabu Search

Optimization of a Cluster-Based Energy Management System Using Deep Reinforcement Learning Without Affecting Prosumer Comfort: V2X Technologies and Peer-to-Peer Energy Trading

Potential Evaluation Method for Aggregated Demand Response Resources Based on User Pattern Recognition

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