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Article
Engineering, Electrical & Electronic
Feixiong Chen et al.
Summary: “This paper proposes a distributed robust cooperative scheduling method for the multi-region electricity-natural gas-heat integrated energy systems (IESs), considering the dynamic characteristics and uncertainties. The method effectively addresses the challenges of information privacy and decision-making independence in multi-region IES.”
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2023)
Article
Green & Sustainable Science & Technology
Haifeng Qiu et al.
Summary: This paper offers a reformulated multistage mixed-integer robust optimization method for regional power grid scheduling. A scheduling model is established considering real-world projects, and a dedicated reformulation algorithm is proposed to solve the intractable multistage mixed-integer robust optimization model. Numerical tests verify the superiority and applicability of the proposed method.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2023)
Article
Green & Sustainable Science & Technology
Qinghan Sun et al.
Summary: In this article, a new model for distributed energy systems (DES) is proposed, which takes into account the delay and storage features of pipeline heat migration and heat transfer between fluids. A dispatch problem considering hybrid regulation of fluid flow rates and temperatures is established, and a decentralized gradient descent method with the Alternating Direction Method of Multipliers (ADMM) is proposed to optimize the DES in a fully decentralized manner. Case studies on two test systems validate the effectiveness of the proposed model and method in reducing renewable energy curtailment by 17.3% and 27.0% respectively.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2023)
Article
Energy & Fuels
Yang Li et al.
Summary: A community integrated energy system (CIES) is a significant component of the energy internet and smart city, providing a new solution to energy utilization and environmental pollution. To address uncertainties in renewable energy generation (RGs), a data-driven two-stage distributionally robust optimization (DRO) model is constructed. A generative adversarial network based on the Wasserstein distance is proposed to generate scenarios for RGs, and an integrated demand response mechanism is developed to promote renewable energy consumption.
Article
Energy & Fuels
Zhengmao Li et al.
Summary: This paper proposes a coordinated restoration method for the renewable energy-integrated multi-energy distribution system (MDS) to address the threats of low-probability but high-impact extreme events, such as floods, earthquakes, and hurricanes, to the security of the energy system. The method comprehensively models the MDS restoration with coupled power and thermal network constraints, utilizing thermal inertia and smart buildings' thermal demand response as buffers to reduce post-disaster energy supply cost. Preparation and load recovery stage measures are employed for efficient and reliable system restoration. Multiple uncertainties are dealt with through a risk-averse two-stage stochastic programming approach. Simulation results validate the effectiveness and superiority of the method.
Article
Energy & Fuels
Weiwei Li et al.
Summary: This paper proposes an optimal planning method for regional integrated electricity-heat systems with data centers (DC-RIEHS) considering wind power uncertainty. The method includes a novel data center model and a three-stage distributionally robust chance-constrained (DRCC) planning model. Simulation results show that the flexible resources in the data center can reduce system costs and actively cope with wind power uncertainty. The proposed DRCC method exhibits better out-of-sample performance under wind uncertainty.
Article
Thermodynamics
Mikhail Skalyga et al.
Summary: Combined heat and power (CHP) plants in district heating systems can produce both heat and electric power simultaneously. They can participate in electricity markets, but operational decisions need to be made under uncertain electricity prices. This paper develops a distributionally robust short-term operational model for CHP plants in the day-ahead electricity market, considering the heating network and temperature dynamics. A case study shows the reliability and profit gain of the proposed operational strategy for the CHP producer.
Article
Engineering, Electrical & Electronic
Junyi Zhai et al.
Summary: This paper focuses on the decentralized adjustable robust operation problem for multi-area integrated electricity and natural gas systems (IEGS). It proposes an asynchronous alternating direction method of multipliers (ADMM) based decentralized model that allows each area to perform local updates with information from a subset of neighbors, solving the synchronization and heterogeneity issues of existing distributed algorithms. The combination of linear decision rules (LDRs) and automatic generation control (AGC) systems is also introduced to effectively handle renewable energy uncertainty.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2023)
Article
Energy & Fuels
Hong Tan et al.
Summary: Traditional energy supplies in rural areas are inefficient and environmentally unfriendly. This paper proposes a cooperative operation framework for an integrated rural energy system with greenhouses (IRES-GH) and introduces a novel forecast scenario-based robust energy management method. The method effectively hedges the uncertainty of electric load and wind power output, improving energy utilization efficiency and reducing emissions.
Article
Energy & Fuels
Xihai Zhang et al.
Summary: Peer-to-peer (P2P) energy trading has economic benefits for prosumers, but the intermittency of photovoltaic (PV) poses challenges. This paper proposes a data-driven distributionally robust optimization (DRO) approach for P2P energy trading to minimize the operation cost and handle the randomness of PV generation.
Article
Energy & Fuels
Chenghan Zhou et al.
Summary: A two-stage robust optimization method is proposed for buildings' space heating loads in an integrated community energy system. The hierarchical relationship between the ICES operator and consumers is formulated using bi-level optimization to achieve optimal heating pricing strategy. The thermal inertia of SHLs is modeled to provide heating demand response based on optimal heating sale prices. The ICES operator decides optimal energy purchase schedules from upper energy systems using a robust optimization method. The whole optimization model is transformed into a MILP based on various mathematical techniques. Numerical results show the effectiveness of the proposed model in balancing the interests of the ICES operator and consumers, and the improved profit using the robust optimization method.
Article
Thermodynamics
Kaile Zhou et al.
Summary: This study proposes a hybrid robust decentralized energy management framework for the optimal day ahead scheduling of interconnected MEMGs. It utilizes a decomposition strategy based on ADMM for privacy protection and incorporates DRO and RO to effectively deal with multiple uncertainties. Experimental results show that the coordination of multiple energy carriers can significantly lower the total cost and carbon emission cost. The used ADMM owns excellent performance and comparative analysis further demonstrates the predictability and superiority of the proposed HyRO.
Article
Thermodynamics
Fengwu Han et al.
Summary: Hybrid energy storage system (HESS) is advantageous in dealing with the uncertainty of renewable energy and enhancing the stability of regional integrated energy systems (RIES). However, the existing uncertainty scheduling methods for HESS often overlook robustness and nonanticipativity, leading to impractical scheduling strategies. Thus, this study establishes a HESS with multiple storage devices and an independent hybrid energy storage operator (IHESO) to provide energy services. A novel multi-stage distributionally robust optimization (MSDRO) method is developed to maximize HESS revenue, taking into account robustness and nonanticipativity. The stochastic dual dynamic integer programming (SDDiP) algorithm is then introduced to solve the complex nested problems of multi-stage systems. Simulation results of a real system under different scenarios demonstrate that the MSDRO model and SDDiP algorithm can ensure effective operation with robustness and nonanticipativity, leading to improved HESS economy, stability, increased renewable energy consumption, and reduced carbon emissions.
Article
Engineering, Electrical & Electronic
Zhen-Long Li et al.
Summary: This paper proposes a day-ahead scheduling model for the coordination of communication network, multi-energy microgrids, and distribution network using the proposed cyber-physical-social system framework. The scheduling takes into account social benefits and demand response is used for power consumption and bandwidth allocation. The coordination is carried out through the interaction between different regions, and the optimization problem is solved using the parallel enhanced self-adaptive Alternating Direction Method of Multipliers algorithm.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Hongwei Li et al.
Summary: This paper proposes a distributionally robust optimal dispatching method for integrated electricity and heating system, which addresses the uncertainties from wind power forecast errors and operating characteristics of electric vehicles (EVs) using an improved Wasserstein metric. The proposed model includes a Wasserstein ball centered at the empirical distribution for wind power forecast errors and an adjustable uncertainty set for EV operating characteristics to quantify uncertainties more flexibly. The computational efficiency is improved by introducing an improved Wasserstein metric based on extreme scenarios DROD model. Out-of-sample analysis on the IEEE 118-bus test system confirms the robustness and computational superiority of the proposed model.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Feixiong Chen et al.
Summary: This paper proposes a distributed two-stage robust optimization (TRO) scheduling strategy for integrated energy systems (IES) to address uncertainties caused by renewable energy and loads. The strategy ensures data privacy of individual subsystems and utilizes gas inertia and biogas-wind renewables. The simulation results of a three-subsystem joint IES demonstrate the effectiveness of the proposed strategy.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Alessandro Francesco Castelli et al.
Summary: This work proposes a robust operational optimization approach for Aggregated Energy Systems (AES) on three different time scales. The approach includes a rolling horizon algorithm based on an Affine Adjustable Robust Optimization model. The proposed methodology is tested on three real-world case studies and shows promising results in meeting yearly constraints and managing seasonal storage.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2023)
Article
Energy & Fuels
Chao Sun et al.
Summary: This paper investigates the network-aware peer-to-peer multi-energy scheduling and trading scheme. A decentralized dual-loop P2P-MEST scheme with dynamically updated loss factors is proposed to accurately consider energy transfer loss and indirectly handle physical operational constraints.
Article
Energy & Fuels
Chang Yang et al.
Summary: This paper proposes a distributed conditional-distributionally robust coordination method for an electricity and flexibility-enhanced district heating system. The method considers the inherent dependency between wind farm forecasting error and forecast output information, and uses a novel reformulation method to guarantee feasibility. Additionally, a double-layer distributed algorithm is proposed to protect the privacy of system operators and solve the problem in a distributed fashion.
Article
Green & Sustainable Science & Technology
Xiaohui Yang et al.
Summary: To address the negative impact of uncertainties in renewable energy generation and load on microgrid operation, a multi-time scale optimal dispatch strategy combining robust optimization and rolling optimization is proposed. The simulation results show that compared with the traditional optimal dispatching strategy, there are reductions in operating cost and peak-to-valley difference of electric load by 5.4% and 14.7%, respectively, and an increase in the utilization of renewable energy by 4.8%.
Article
Engineering, Electrical & Electronic
Haifeng Qiu et al.
Summary: This paper proposes a hierarchical three-stage robust optimization method to tackle the cooperative strategic bidding problem with uncertainties in smart community grids. The method establishes a novel bidding model based on a bilayer coordination framework and ensures privacy preservation for each prosumer to conduct its own robust scheduling. Numerical tests validate the applicability and superiority of the proposed method for power bidding in smart community grids.
IEEE TRANSACTIONS ON SMART GRID
(2023)
Article
Energy & Fuels
Yuwei Wang et al.
Summary: A new optimization model is proposed for integrating power-to-gas, which can improve both the economic and environmental benefits of the system while maintaining low conservatism and computational complexity.
Article
Thermodynamics
Zhuoya Siqin et al.
Summary: This paper proposes a P2G-CCHP microgrid system integration framework and solves the economic dispatch problem through a two-stage distributionally robust optimization model. The model considers environmental cost and improves the system stability and economy by introducing P2G devices.
Article
Engineering, Electrical & Electronic
Junyi Zhai et al.
Summary: This paper investigates the distributed adjustable robust optimal power and gas flow (OPGF) model for integrated electricity and natural gas systems (IEGS). Linear decision rules (LDRs) are used to propose an improved adjustable robust model that can effectively deal with renewable energy uncertainty and reduce solution conservatism. Two tailored alternating direction method of multipliers (ADMM) based distributed optimization frameworks are presented to preserve information privacy and decision-making independence of subsystems.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Zhiping Cheng et al.
Summary: This paper proposes a robust optimal scheduling strategy for the coordinated operation of CCHP microgrid. The strategy considers uncertainties at both the day-ahead and intraday scales and utilizes a rolling optimization approach to obtain optimal scheduling results.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2022)
Article
Energy & Fuels
Xuewei Wu et al.
Summary: This paper proposes a distributionally robust unit commitment method for an integrated electricity and heat system. The method operates the district heating systems under variable flow and variable temperature (VFVT) with bi-directional mass flow, which increases the flexibility capacity for renewable energy accommodation. The distributionally robust optimization scheme also enhances the operation flexibility of the system under high penetration of renewable energy.
Article
Energy & Fuels
Junyi Zhai et al.
Summary: This paper focuses on the distributionally robust energy management problem for Multi-energy microgrid (MEMG). A data-driven Wasserstein distance-based distributionally robust joint chance-constrained (DRJCC) energy management model is proposed, utilizing various flexible resources in different energy sectors for uncertainty mitigation. An optimized conditional value-at-risk (CVaR) approximation (OCA) formulation is used to make the model tractable, and an iterative sequential convex optimization algorithm is tailored to reduce solution conservatism by tuning OCA. Numerical results demonstrate the effectiveness of the proposed model.
Article
Engineering, Electrical & Electronic
Lingmin Chen et al.
Summary: This paper proposes a flexible participation mode of integrated demand response (IDR) resources and establishes a robust scheduling optimization model considering IDR in micro-energy grid, aiming at the uncertainties of renewable energy output and electric, cooling, and heating loads. The results show that the robust optimization model improves the system's ability to resist uncertain risks, and integrated demand response enhances the model's economy and the self-sufficiency of the micro-energy grid.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Gaohang Zhang et al.
Summary: This paper proposes a decentralized robust optimization framework for the unit commitment and reserve allocation into bulk AC/DC interconnected systems. The scheduling problem is decomposed and handled by the analytical target cascading (ATC) technique, with the upper-level master problem coordinating and updating the transmission plan of the high-voltage direct current (HVDC) tie-line, and the lower-level regional sub-problems formulated as robust security-constrained unit commitment models.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2022)
Article
Energy & Fuels
Haifeng Qiu et al.
Summary: This paper proposes a multistage robust unit commitment (UC) method to deal with the uncertainty in power grids caused by source-load power and line outage. The method includes a multistage robust UC model and a customized multi-cut decomposition algorithm. Computational tests on real-size power grids validate the effectiveness and superiority of this method in guiding system operations.
Article
Energy & Fuels
Caishan Guo et al.
Summary: Cloud computing platforms and data centers are critical in modern society, acting as large energy consumers in power grids. By integrating on-site renewable energy sources and storage systems, data centers can become energy prosumers. Future research will focus on the integration of data centers and smart grids to optimize energy consumption and actively participate in grid planning and operation.
Article
Energy & Fuels
Arsalan Najafi et al.
Summary: This paper proposes a hybrid decentralized robust optimization-stochastic programming (DRO-SP) model to coordinate the management of EVAGG and EH. By combining stochastic programming and robust optimization, uncertainties in entity scheduling are addressed, achieving economic scheduling while ensuring information privacy between entities.
Review
Green & Sustainable Science & Technology
Menglin Zhang et al.
Summary: This review provides a comprehensive overview of the modeling and solution methods for the optimal operation of integrated electricity and heat systems (IEHS). It identifies the environmental benefits, cost reduction, and high penetration levels of renewables that can be achieved through optimal operation. The study categorizes solution methods into four groups based on optimization problem properties and compares their characteristics, advantages, and limitations.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2021)
Article
Engineering, Electrical & Electronic
Alessio La Bella et al.
Summary: This paper explores the provision of ancillary services in smart energy systems, focusing on optimizing the distribution of flexibility effort to prosumers by an Energy Service Provider. A scalable strategy is proposed to account for integer decision variables and reduce combinatorial complexity while maintaining privacy. Simulation results demonstrate the effectiveness of the approach in terms of scalability and quality of feasible solutions.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2021)
Article
Energy & Fuels
Weiye Zheng et al.
Summary: This paper investigates the price incentives offered by EPNs to encourage DHNs to fully utilize pipeline energy storage in order to improve energy efficiency. A bi-level model is used to address the individual interests of EPNs and DHNs, and a simplified OTF model is proposed to protect the privacy of DHNs in distributed operation. The introduced incentive-based coordination mechanism effectively improves social welfare by lowering total costs in both EPNs and DHNs, reaching an equilibrium of Stackelberg competition.
Article
Green & Sustainable Science & Technology
Ampolu Maneesha et al.
Summary: Optimization algorithms, particularly distributed ones, are crucial in achieving optimal solutions in Smart Grid. This paper surveys the Alternating Direction Method of Multipliers (ADMM) algorithm and its applications in Smart grid operation and control, highlighting research gaps and future directions.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2021)
Article
Thermodynamics
Yanbin Li et al.
Summary: This paper presents a CCHP-P2G microgrid system combining a P2G device with traditional CCHP microgrid, and proposes a DSRO model to consider the uncertainties of wind power and multiple DRPs. Simulations show the advantages of the system in terms of stability, economy, and operating cost reduction.
Article
Engineering, Electrical & Electronic
Weiye Zheng et al.
Summary: The coordination efficiency between different energy sectors with privacy preservation has become a technical bottleneck in the distributed operation of the integrated electricity and heat systems (IEHS). This article introduces an equivalent model-based non-iterative solution for this issue, demonstrating its effectiveness and efficiency through case studies of two test systems.
IEEE TRANSACTIONS ON SMART GRID
(2021)
Article
Energy & Fuels
Wei Gan et al.
Summary: This paper proposes a decentralized computation method for the robust operation of multi-area joint regional-district integrated energy systems, aiming to enhance system flexibility and wind power integration capability. Compared to traditional centralized methods, the proposed method has obvious economic advantages in scenarios with high wind power penetration levels.
Article
Energy & Fuels
Peng Sun et al.
Summary: The paper proposes a robust coordinated optimization method for MES considering thermal inertia uncertainty (TIU), aiming to improve the energy efficiency and flexibility of the system. By modeling and studying thermal inertia, a two-stage robust optimal scheduling model considering TIU is developed, and a case study is conducted in a real MES in Northeastern China. Simulation results show that considering TIU can reduce operating costs and enhance heat storage capacity of the system, benefiting wind power integration.
Article
Thermodynamics
Junjie Zhong et al.
Summary: A distributed synergistic model with min max-min robust optimization is proposed for a 3-block integrated energy system, which effectively handles multiple uncertainties and accelerates the solution process with the developed C&CG-AOP algorithm. The simulation results show that the constructed uncertainty set considering spatial-temporal correlation and symmetry can reduce operating costs.
Article
Engineering, Electrical & Electronic
Yizhou Zhou et al.
Summary: An optimized model for the resilient operation of integrated electricity and heat energy distribution systems in extreme weather events is proposed. The strengthened ambiguity set provides a more accurate characterization of true probability distribution. Numerical results validate the enhanced resilience, reduced conservatism, and computational efficiency of the proposed approach.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Junjie Zhong et al.
Summary: This paper investigates the uncertainty handling methods of integrated electricity and heat systems (IEHS) using a hybrid stochastic/robust optimization model, and utilizes BADMM to decouple the original IEHS model, thereby enhancing system flexibility. Additionally, the simulation results show the impact of uncertainty and spatial-temporal correlativity on IEHS system state, and the distributed model proposed based on BADMM can improve convergence and realize cooperative operation of IEHS.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2021)
Article
Automation & Control Systems
Alessandro Falsone et al.
Summary: This study focuses on constraint-coupled optimization for multiple cooperative agents communicating over a time-varying network. A distributed proximal minimization algorithm is proposed to converge to an optimal solution under certain convexity and connectivity assumptions. The algorithm's performance is demonstrated on a numerical example involving a charging scheduling problem for a fleet of plug-in electric vehicles.
IEEE CONTROL SYSTEMS LETTERS
(2021)
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IEEE TRANSACTIONS ON SMART GRID
(2020)
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IEEE TRANSACTIONS ON POWER SYSTEMS
(2020)
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Lu Wang et al.
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Jin Tan et al.
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Shuai Lu et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2020)
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Green & Sustainable Science & Technology
Shuai Lu et al.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2020)
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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
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IEEE TRANSACTIONS ON POWER SYSTEMS
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