Regional active distribution network planning study based on robust optimization

Jiang, Zhicheng; Yu, Qingguang; Xiong, Yufeng; Li, Le; Liu, Yuming

doi:10.1016/j.egyr.2021.06.050

Cited by 5 publications

(3 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wan et al [13] proposed a mixed-integer second-order cone programming approach to optimizing active distribution networks, by considering a collaboration among distributed flexible resources and conducting a case analysis, resulting in a significant reduction of 47.9% in the daily operating costs and 75.2% in carbon emissions for ADNs. Jiang et al [14] proposed a robust optimization model for regional network planning, by incorporating economic indicators to evaluate the benefits of new energy subsidies and operational efficiency, while also summarizing the physical constraints of various devices in regional active distribution networks. Kong et al [15] proposed an optimal strategy for targeting active management costs related to source-network-load, investigated cost optimization strategies in active management, with a focus on load response and pricing incentives, and developed an opportunity-constrained optimization model that incorporates Monte Carlo simulations to address uncertainties.…”

Section: Research Status Of Active Distribution Networkmentioning

confidence: 99%

Economic Scheduling Model of an Active Distribution Network Based on Chaotic Particle Swarm Optimization

Xu,

Liu,

Cui

et al. 2024

Information

View full text Add to dashboard Cite

With the continuous increase in global energy demand and growing environmental awareness, the utilization of renewable energy has become a worldwide consensus. In order to address the challenges posed by the intermittent and unpredictable nature of renewable energy in distributed power distribution networks, as well as to improve the economic and operational stability of distribution systems, this paper proposes the establishment of an active distribution network capable of accommodating renewable energy. The objective is to enhance the efficiency of new energy utilization. This study investigates optimal scheduling models for energy storage technologies and economic-operation dispatching techniques in distributed power distribution networks. Additionally, it develops a comprehensive demand response model, with real-time pricing and incentive policies aiming to minimize load peak–valley differentials. The control mechanism incorporates time-of-use pricing and integrates a chaos particle swarm algorithm for a holistic approach to solution finding. By coordinating and optimizing the control of distributed power sources, energy storage systems, and flexible loads, the active distribution network achieves minimal operational costs while meeting demand-side power requirements, striving to smooth out load curves as much as possible. Case studies demonstrate significant enhancements during off-peak periods, with an approximately 60% increase in the load power overall elevation of load factors during regular periods, as well as a reduction in grid loads during evening peak hours, with a maximum decrease of nearly 65 kW. This approach mitigates grid operational pressures and user expense, effectively enhancing the stability and economic efficiency in distribution network operations.

show abstract

Section: Research Status Of Active Distribution Networkmentioning

confidence: 99%

Economic Scheduling Model of an Active Distribution Network Based on Chaotic Particle Swarm Optimization

Xu,

Liu,

Cui

et al. 2024

Information

View full text Add to dashboard Cite

show abstract

“…Minimizing the installation costs of power plants, high-voltage/low-voltage substations, and feeders, feeders' power transmission costs + Minimizing the storage power cost, power losses' costs in feeders, feeder failures' costs [98] PV and wind generations, Robust optimization Minimizing the annual costs of the regional distribution networks [103] Wind generation, outages, La Niña and El Niño events (a long-term warming happening for the central and eastern Pacific and vice versa)…”

Section: Hybrid Stochastic/robustmentioning

confidence: 99%

Applications of Probabilistic Forecasting in Smart Grids: A Review

Khajeh

Laaksonen

2022

Applied Sciences

View full text Add to dashboard Cite

This paper reviews the recent studies and works dealing with probabilistic forecasting models and their applications in smart grids. According to these studies, this paper tries to introduce a roadmap towards decision-making under uncertainty in a smart grid environment. In this way, it firstly discusses the common methods employed to predict the distribution of variables. Then, it reviews how the recent literature used these forecasting methods and for which uncertain parameters they wanted to obtain distributions. Unlike the existing reviews, this paper assesses several uncertain parameters for which probabilistic forecasting models have been developed. In the next stage, this paper provides an overview related to scenario generation of uncertain parameters using their distributions and how these scenarios are adopted for optimal decision-making. In this regard, this paper discusses three types of optimization problems aiming to capture uncertainties and reviews the related papers. Finally, we propose some future applications of probabilistic forecasting based on the flexibility challenges of power systems in the near future.

show abstract

“…Fuzziness is usually quantified using membership functions. To address the optimization dispatch problem caused by supply-demand uncertainty, various methods are commonly used for model establishment, including reserve capacity [5],robust optimization dispatch [6][7][8][9][10], and stochastic optimization dispatch [11][12][13][14][15] Reference [16]describes robust optimization as representing random variables in the form of sets, which requires the worst-case scenario to satisfy the requirements, making it overly conservative and potentially lacking an optimal solution. Common stochastic optimization methods include scenario-based stochastic optimization [17][18][19][20] and chance-constrained programming-based stochastic optimization [21][22].…”

Section: Introductionmentioning

confidence: 99%

A Joint Optimization Approach on Monthly Balancing Mechanism and Unit Maintenance Scheduling

Huie,

Dagui,

Shaowu

et al. 2024

IEEE Access

View full text Add to dashboard Cite

In the Chinese electricity market, the deviation between monthly actual electricity demand and generation plans has become particularly prominent amidst deepening power system reform. This study focuses on the uncertainties of wind power and photovoltaic power generation in the medium to long term electricity market. It also examines the impact of maintenance scheduling for conventional generation units on the implementation of medium to long-term generation scheduling plans. The article proposes a model for optimizing the joint generation and maintenance scheduling on a monthly basis. The model is based on deviation handling and employs upward and downward price incentive mechanisms. The aim is to minimize the cost of adjusting the deviation of monthly transaction contract quantities. To deal with the challenges posed by source-load uncertainties, the model utilizes fuzzy chance-constrained programming. The research findings confirm that the proposed model can significantly reduce system operating costs, curtailment of wind power and photovoltaic power, and enhance the market integration of renewable energy. Additionally, it can achieve uniformity in the completion rate of monthly contract quantities for conventional thermal power units, ensuring a balanced power system in terms of economy and security. The study presents an optimization method for executing contract quantities in the electricity market, contributing to improved efficiency in power resource allocation and reduced energy losses. The research enriches the theoretical foundation of power system scheduling and provides practical operational guidelines for power dispatch, making it significant for promoting sustainable development in the power industry and ensuring energy security.INDEX TERMS fuzzy chance-constrained programming, balancing mechanism, long-term transactions, maintenance Scheduling, contract power.

show abstract

Regional active distribution network planning study based on robust optimization

Cited by 5 publications

References 2 publications

Economic Scheduling Model of an Active Distribution Network Based on Chaotic Particle Swarm Optimization

Economic Scheduling Model of an Active Distribution Network Based on Chaotic Particle Swarm Optimization

Applications of Probabilistic Forecasting in Smart Grids: A Review

A Joint Optimization Approach on Monthly Balancing Mechanism and Unit Maintenance Scheduling

Contact Info

Product

Resources

About