Uncertainty Of Renewable Energy Research Articles

UHVDC transmission line diagnosis method for integrated community energy system based on wavelet analysis

With the large-scale development of renewable energy power, China has faced with the challenges of the reverse regional distribution of wind and solar resources and power load, as well as the intermittency and randomness of renewable energy power. Therefore, China is vigorously developing ultra-high voltage direct current (UHVDC) transmission technology to solve the problem of insufficient flexibility caused by the uncertainty of renewable energy and the fluctuation of multi-energy loads in integrated community energy systems. UHVDC plays an increasingly pivotal role in the west-east transmission system in China’s power system due to its high transmission capacity and long transmission distance. Once the fault occurs in the ultra-high voltage direct (UHVD) transmission line, quick and accurate fault location identification is of great significance. Hence, this paper proposes a UHVDC transmission line diagnosis method based on wavelet analysis for integrated community energy systems. Wavelet transform (WT) is used to decompose the transient signal on a multi-scale, and then power systems computer-aided design (PSCAD) software is utilized for simulation calculation to obtain the singular spectrum entropy of each layer and facilitate wavelet transformations for signal denoising with advanced tools such as MATLAB. The prediction results can distinguish outside the rectification side fault, within the rectification side fault, and outside the inverter fault with an accuracy of 100%. A large number of simulations demonstrate that combining singular spectrum entropy with support vector machines (SVM) has emerged as a robust technique for integrated community energy systems, suggesting its potential as a standard method in UHVDC transmission line diagnosis. This study is of significant reference for realizing the complementarity of multiple types of power supply and ensuring a reliable power supply.

Multistage Stochastic optimization for mid-term integrated generation and maintenance scheduling of cascaded hydroelectric system with renewable energy uncertainty

The uncertainties resulting from the escalating penetration of renewable energy resources pose severe challenges to the efficient operation of modern power systems. Hydroelectricity is characterized by its flexibility, controllability, and reliability, and thus becomes one of the most ideal energy resources to hedge against such uncertainties. This paper studies the mid-term integrated generation and maintenance scheduling of a cascaded hydroelectric system (CHS) consisting of multiple cascaded reservoirs and hydroelectric units. To precisely describe the mid-term water regulation policies, the hydraulic coupling relationship and water-energy nexus of CHS are incorporated into the proposed optimization model. The uncertainties of natural water inflow and the power outputs of wind/solar energy generation are taken into consideration and captured via a stochastic process modeled by a scenario tree. A multistage stochastic optimization (MSO) approach is developed to coordinate the complementary operations of multiple energy resources, by optimizing the mid-term water resource management, generation scheduling, and maintenance scheduling of CHS. The proposed MSO model is formulated as a large-scale mixed-integer linear program that presents significant computational intractability. To address this issue, a tailored Benders decomposition algorithm is developed. Two real-world case studies are conducted to demonstrate the capability and characteristics of the proposed model and algorithm. The computational results show that the proposed MSO model can exploit the flexibility of hydroelectricity to efficiently respond to variable wind and solar power, and reserve water resources for the generation in peak months to reduce the consumption of fossil fuel. The proposed solution approach also exhibits promising computational efficiency when handling large-scale models.

Hydrothermal–pumped-storage scheduling considering renewable energy uncertainty by improved cheetah optimization

The growth of renewable wind and solar energy in modern power systems affects the short-term scheduling of hydrothermal power generation. This article presents the optimum day-ahead scheduling of wind–solar–hydrothermal systems with pumped-storage plants (PSPs). To assess the contribution of PSPs in decreasing the generation costs of a renewable integrated power system, a scheduling model is formulated that considers different security constraints. An improved cheetah optimizer (ICO) is implemented on a renewable integrated test power system to solve the short-term optimum wind–solar–pumped-storage plant–hydrothermal scheduling (OWSPHTS) problem, taking into account all thermal, hydraulic and network constraints. Optimum solutions are obtained with different objectives considering renewable uncertainties. The proposed ICO solution method is compared with the similar algorithms in terms of optimal fuel costs, emissions, convergence success rate and computation time. The comparative solutions show that the introduced optimization technique is efficient for solving the real-world OWSPHTS problem.

Electrification-driven circular economy with machine learning-based multi-scale and cross-scale modelling approach

Community energy systems, integrating electricity storage, smart transportation, and flexible energy interactions can mitigate renewable energy intermittency and uncertainty, and stabilize local grids. The battery circular economy, involving cascade use, reuse and recycling, aims to reduce energy storage costs and associated carbon emissions. However, developing multi-scale and cross-scale models based on physical mechanisms faces challenges due to insufficient expertise and temporal discrepancies among subsystems. This study proposes a general machine learning approach for a source-grid-demand-storage community with a battery circular economy, streamlining data preparation, model training, validation, and testing. Machine learning models replace traditional physical models for energy consumption, renewable energy supply, and battery cycling ageing, improving computational efficiency. Furthermore, a battery circular economy principle is proposed, which suggests reusing 80 % capacity retired electric vehicle batteries in buildings and recycling at 60 % capacity. The study validates machine learning-based surrogate models against traditional physical models. Results show an 82.5 % reduction in computational time, from 40 to 7 min without significant deviation in techno-economic-environmental performance (relative difference ≤3 %). This study offers a framework for machine learning in the 'source-grid-demand-storage' system within a battery circular economy, enhancing computation efficiency and accuracy for low-carbon transitions and informing multi-scale, cross-scale model advancement.

Selection of an economics-energy-environment scheduling strategy for a community virtual power plant considering decision-makers’ risk attitudes based on improved information gap decision theory

To address the impact of decision-makers' risk attitudes (DMRA) and uncertainties on dispatch strategy in community integrated energy systems, a multi-objective dispatch model of a community virtual power plant (CVPP) is proposed. Firstly, a novel CVPP model that considers DMRA and a multi-objective satisfaction model of economics-energy-environment are built. Secondly, the information gap decision theory (IGDT) model is improved by considering uncertainties of renewable energy, loads, and DMRA. Thirdly, the VIKOR method is improved by using the weights from multiple objective functions derived by combining the entropy weight and full consistency method. And the conservative and risky strategy selection methods in the VIKOR method are used to select the optimal strategy from the obtained multi-objective Pareto solution set that satisfies decision-makers with risk preferences. Thus, the impact of DMRA on optimal strategy selection is fully described. Finally, the proposed model's effectiveness is validated using a multi-scenario example of a residential area. The results indicate that: 1) The novel CVPP provides realistic scheduling strategies based on the DMRA. 2) After implementing demand response, the resident cost and net carbon emissions are reduced by 9 % and 91 %, respectively. Energy supplier profit and renewable energy utilization rate are increased. The constructed IGDT model also improves multiple objectives. 3) The improved IGDT model's uncertainty and deviation factors allow for diverse scheduling strategies. Simultaneously, the improved VIKOR method provides a new method for decision-makers to select strategies. The model is a guide for selecting scheduling strategies and a way of encouraging the usage of renewable energy.

Power generation–network–load–energy storage co-planning under uncertainty

With the aggregation of renewable energy in the power system, the uncertainty caused by the renewable energy affects the planning and operation of power systems. Meanwhile, the existing planning models fail to consider renewable energy uncertainty methods, specifically concerning renewable energy confidence and future possible scenarios; thus, a confidence-based scenario cluster method is presented. A novel generator, network, load, and energy storage (GNLS) co-planning model is proposed in the paper. First, a confidence-based scenario cluster is built, which can reflect uncertainties by clustering and analyzing wind, solar, and load. Second, the proposed model focuses on load and energy storage co-planning, and in addition, relevant flexible indices are used to assess the model. Finally, the GNLS co-planning model is built as a bi-level stochastic model on continuous time scales. The model is solved using the Benders decomposition algorithm. The method in this paper is validated using an IEEE RTS 24-bus and a real test system in China to demonstrate the reduction in renewable energy curtailment and optimization of economic factors in power system planning.

Peer-to-peer electricity trading model for urban virtual power plants considering prosumer preferences and power demand heterogeneity

Peer-to-peer electricity trading has emerged as a critical way to meet the varied demands of prosumers within urban virtual power plants. To better support supply-demand balance and ensure the stable, low-carbon and efficient peer-to-peer electricity trading, both prosumer trading preferences and power demand heterogeneity need to be considered. This study proposes a peer-to-peer electricity trading model for urban virtual power plants that considers both preference of prosumers and power demand heterogeneity. First, to effectively meet the trading demands of diverse prosumers and enhance the penetration of renewable energy, prosumers are classified based on variations in financial returns, green electricity preferences and risk avoidance. Power demand is further classified based on the trading preferences of prosumers. Then, a peer-to-peer electricity trading model incorporating a game model and a market bidding mechanism is proposed to enable efficient and dynamic matching between supply and demand. Finally, to mitigate the impact of renewable energy uncertainty on peer-to-peer electricity trading, real-time auctions are implemented across multiple virtual power plants. The results show that the proposed model is capable of more effectively accommodating the diverse demands of prosumers, and facilitates a more stable, low-carbon and efficient peer-to-peer electricity trading.

AUGMECON2-based multi-objective optimization of virtual power plant considering economical and security operation of the distribution networks

With the rapid development of massive multivariate distributed energy resources on the demand side, the distribution network is transforming from traditional passive to active management. By constructing the virtual power plant, distribution network operators will handle the management and transactions of those distributed resources as their participation in the electricity market becomes normal. Moreover, distributed resources will affect the operating economy and security of the distribution network, such as network losses and peak loads. Thus, distribution network operators must consider both the security of distribution networks as well as the economics of distributed resources. To address this issue, a virtual power plant with the distribution network operator as the stakeholder has been built. A multi-objective optimization model based on the improved augmented ε-constraint method (AUGMECON2) method is then proposed. The key objective is to maximize operating benefits while also accounting for network losses and peak-to-valley load disparity of the slack bus. To address the uncertainty of renewable energy, a dynamic reserve capacity calculation approach is developed, taking into account the economics as well as transaction reliability. The Pareto frontier of the multi-objective optimal problem is obtained by AUGMECON2, and a compromised solution considering fairness is proposed. Finally, the proposed method is verified with case studies based on the modified IEEE33-node distribution network system.

An iterative auction-based method for multi energy trading in a microgrid considering renewable energy uncertainties

Due to the significant strides made in renewable energy generating units, they are now an appealing choice for supplying energy with various benefits. Furthermore, the aggregation of different energy generators and resources and the rise of energy conversion facilities, like combined heat and power (CHP), have paved the way for a new category of multi-energy prosumers. This research presents an innovative model for a local energy market structure consisting of an aggregator, prosumers, electricity, gas, and heat utility grids, and consumers. The interplay between these entities is handled as an auction model with proposed bidding strategies in which each player participates to maximize their own objective function. The numerical analysis reveals the impact of the pricing strategy on the prosumers' willingness to provide more energy during peak demand periods. Furthermore, the study highlights the potential for mutually beneficial collaborations between prosumers, which leads to reduced energy costs.

Optimal scheduling of integrated energy system considering renewable energy uncertainties based on distributionally robust adaptive MPC

The uncertainty of renewable energy makes the optimal scheduling of integrated energy systems (IES) challenging and complex. The paper suggests a novel two-stage optimized scheduling model based on distributionally robust adaptive model predictive control (DRAMPC), which effectively improves scheduling accuracy and efficiency while taking robustness and economy into account. In the day-ahead stage, the multi-objective distributionally robust optimization (MODRO) model is composed based on the imprecise dirichlet model (IDM), which incorporates robustness and operating cost metrics into the optimization objective to achieve synergistic optimization of robustness and economy. The adaptive step double-loop model predictive control (ASDL-MPC) utilizes the dual closed-loop feedback of renewable energy output prediction errors and operating cost prediction errors to adaptively adjust the rolling time step, correcting the day-ahead scheduling bias while improving scheduling efficiency. The model is resolved using the cross-entropy-radar scanning differential evolution (CE-RSDE) algorithm. The results show that the DRAMPC model can balance economy and robustness, improving the economy of IES by 2.27% while ensuring robustness. The ASDL-MPC intra-day rolling optimization further improves scheduling accuracy and also increases computational efficiency by 6.58%.

A novel stochastic multistage dispatching model of hybrid battery-electric vehicle-supercapacitor storage system to minimize three-phase unbalance

The unbalanced load distribution, the single-phase connection of renewable energy, and the uncoordinated charging of electric vehicles (EVs) will bring a severe issue corresponding to the three-phase unbalance in modern distribution networks. To deal with this issue, a novel multistage optimal dispatching model with the hybrid energy storage system (HESS), consisting of the battery energy storage system (BESS), EV, and supercapacitor (SC), is proposed in this paper. The first stage is conducted on the day-ahead stage, driven by minimizing the total operation cost and relieving power unbalance, the unbalanced penalty cost is introduced into the optimal dispatching model with the electricity purchasing cost and the degradation cost of HESS. Also, the chance-constraint stochastic programming (SP) model with the coordinated operation of HESS is accommodated to handle the diverse uncertainties of renewable energy, load demand, and EV users’ behaviors. In the intra-day operation stage, a rolling optimization-based dispatch model is formulated to re-optimize the operation of the SC for the rapid response of mitigating the real-time three-phase unbalance caused by renewable energy. The numerical experiments are executed on the IEEE 34-bus three-phase test system. Compared with the cases without considering SC and the degradation cost of HESS, the power unbalance in the proposed model is reduced by 3.27% and 3.62% per day at the real-time stage, respectively, while total operation cost is reduced by 46.77 US$ and 350.62 US$ per day, respectively. The results validated that the proposed model is efficient in reducing the three-phase unbalance under multi-time scales, improving the economic operation of the distribution network.

Day-ahead dispatch of electricity-hydrogen systems under solid-state transportation mode of hydrogen energy via FV-IGDT approach

Hydrogen energy has the advantages of being clean and carbon-free, convenient storage, and easy conversion to electrical energy. The use of renewable energy electrolysis for hydrogen production is an important solution to promote clean energy utilization and decarburization in the power and transportation industries. However, hydrogen has a low hydrogen storage density and is prone to explosion. The high cost and risk of hydrogen transportation pose challenges to the promotion and utilization of hydrogen. To address these difficulties, this paper proposes a day-ahead dispatch of electricity-hydrogen systems (EHS) under the solid-state transportation mode of hydrogen energy. Firstly, based on the Van't Hoff equation, the relationship between gas pressure and the reaction temperature is established during the gas-solid conversion process, and a hydrogen energy solid-state transport model based on a magnesium-based hydrogen transport vehicle (MHTV) is proposed. Secondly, a renewable energy uncertainty set is established based on the information-gap decision theory (IGDT) envelope constraint for addressing the uncertainty of renewable energy and a day-ahead bi-level dispatch model of the EHS is constructed based on the solid-state transportation mode of hydrogen energy. Finally, the IGDT approach considering fuzzy variables (FV-IGDT) is presented by using a fuzzy variable membership function, and the proposed bi-level model is transformed into a single-level model for a solution based on this approach. The effectiveness of the proposed model and solution method is verified through simulation on an EHS consisting of a modified IEEE-118 power system, 2 hydrogen production stations (HPS), and 10 hydrogen refueling stations (HRS).

Chance-constrained co-optimization of peer-to-peer energy trading and distribution network operations

Peer-to-Peer (P2P) energy markets enable participants to trade locally produced electrical energy at a mutually agreed-upon price. Distributed Energy Resources (DERs) and flexible loads are fundamental components of P2P energy markets, facilitating the market participants to supply or consume energy in accordance with trading agreements. Consequently, it is vital to account for the stochastic nature of DERs and load demand while designing the P2P energy trading model in order to attain optimal operation of P2P energy markets and secure operation of the distribution network. The scenario-based and robust optimization techniques commonly used to characterize uncertainty suffer from data requirement, computational burden, and solution conservatism standpoints. This paper, therefore, proposes a chance-constrained model for P2P energy trading considering renewable energy uncertainty and distribution network constraints. The uncertain behaviour of renewable generation resources is modelled using chance constraints and integrated with the decision-making process of the P2P energy market and distribution network operation. The chance-constrained P2P energy trading problem is reformulated as a deterministic equivalent problem that can be solved by readily available commercial solvers. The ex-ante and ex-post-performance of the proposed chance-constrained formulation is evaluated on a 15-bus radial distribution network, while the scalability of the proposed formulation is demonstrated on modified IEEE 33-bus and 69-bus test networks. The results showcase the effectiveness of the proposed chance-constrained model in handling the uncertainty and respecting the network constraints while deriving the P2P energy trading outcomes.

A Distributionally Robust Optimization Strategy for a Wind–Photovoltaic Thermal Storage Power System Considering Deep Peak Load Balancing of Thermal Power Units

With the continuous expansion of grid-connected wind, photovoltaic, and other renewable energy sources, their volatility and uncertainty pose significant challenges to system peak regulation. To enhance the system’s peak-load management and the integration of wind (WD) and photovoltaic (PV) power, this paper introduces a distributionally robust optimization scheduling strategy for a WD–PV thermal storage power system incorporating deep peak shaving. Firstly, a detailed peak shaving process model is developed for thermal power units, alongside a multi-energy coupling model for WD–PV thermal storage that accounts for carbon emissions. Secondly, to address the variability and uncertainty of WD–PV outputs, a data-driven, distributionally robust optimization scheduling model is formulated utilizing 1-norm and ∞-norm constrained scenario probability distribution fuzzy sets. Lastly, the model is solved iteratively through the column and constraint generation algorithm (C&CG). The outcomes demonstrate that the proposed strategy not only enhances the system’s peak-load handling and WD–PV integration but also boosts its economic efficiency and reduces the carbon emissions of the system, achieving a balance between model economy and system robustness.

Energy management of multi-microgrid system with renewable energy using data-driven distributionally robust optimization

ABSTRACT By clustering multiple microgrids (MGs), a multi-microgrid (MMG) system plays a significant role in integrating a large amount of renewable generation. However, the large-scale utilization of renewable energy also brings uncertainties to MMG energy management. In this work, a novel comprehensive bi-level MMG energy management model considering uncertainties of renewable energy is first developed which includes unit commitment (UC) problem and other models in the lower MG level. Then considering the non-convexity of the bi-level problem, a decomposition method called analytical target cascading (ATC) is employed to deal with it by decentralization. With regard to the energy management of each individual MG, a two-stage distributionally robust model is developed which describes the uncertainties from probability distribution of renewable generation with a metric-based ambiguity set. Moreover, an efficient solution scheme based on column and constraint generation (CCG) algorithm and a decomposition method without duality is designed to handle the MG energy management problem. Finally, we implement extensive experiments to corroborate the effectiveness of the proposed approach. Particularly, the optimal solution from the proposed method can attain 2.98% less cost compared with that from robust optimization (RO) method and achieve 86.17% less energy trading amount than that in independent mode.

Co-optimizing the smart grid and electric public transit bus system

AbstractAs climate change provides impetus for investing in smart cities, with electrified public transit systems, we consider electric public transportation buses in an urban area, which play a role in the power system operations in addition to their typical function of serving public transit demand. Our model considers a social planner, such that the transit authority and the operator of the electricity system co-optimize the power system to minimize the total operational cost of the grid, while satisfying additional transportation constraints on buses. We provide deterministic and stochastic formulations to co-optimize the system. Each stochastic formulation provides a different set of recourse actions to manage the variable renewable energy uncertainty: ramping up/down of the conventional generators, or charging/discharging of the transit fleet. We demonstrate the capabilities of the model and the benefit obtained via a coordinated strategy. We compare the efficacies of these recourse actions to provide additional managerial insights. We analyze the effect of different pricing strategies on the co-optimization. Noting the stress growing electrified fleets with greater battery capacities will eventually impose on a power network, we provide theoretical insights on coupled investment strategies for expansion planning in order to reduce greenhouse gas (GH) emissions. Given the recent momentum towards building smarter cities and electrifying transit systems, our results provide policy directions towards a sustainable future. We test our models using modified MATPOWER case files and verify our results with different sized power networks. This study is motivated by a project with a large transit authority in California.

Two-stage robust optimization of unified power quality conditioner (UPQC) siting and sizing in active distribution networks considering uncertainty of loads and renewable generators

This paper proposes a robust optimization siting and sizing strategy for the unified power quality conditioner (UPQC) in the active distribution network (ADN) integrated with renewable energy generators. The optimization objectives including investment cost, line loss, and voltage deviation are integrated into an overall cost for both the planning and operating stages. A novel UPQC equivalent power injection model is proposed based on the auxiliary branch method. A set of linear and second-order conic (SOC) constraints are employed to accurately describe the steady-state operation of UPQC. An improved branch current flow model is developed to incorporate radial distribution networks with UPQCs. The UPQC siting and sizing planning is formulated as a mixed integer second-order conic programming (MISOCP) problem. Accounting for the uncertainty in loads and distributed generators, a two-stage robust optimization model is established to address worst-case operational scenarios. A solving strategy is developed based on the strong duality theory and the column-and-constraint generation (C&CG) algorithm, and then commercial solvers can solve the problem accurately and efficiently. Case studies on an IEEE 33-node distribution network demonstrate this strategy's robustness in dealing with loads and renewable energy uncertainties, making it applicable across different UPQC topologies and operation modes. The proposed method has superior performance over the representative meta-heuristic approaches in terms of global optimality as well as an over 7.5 times faster computation speed. This work has significance in enhancing the quality and efficiency of modern power systems highly integrated with renewable energy.

Integrated energy system model with multi-time scale optimal dispatch method based on a demand response mechanism

A flexible integrated energy system (IES) can curb the supply–demand imbalances caused by renewable energy and load uncertainty. This study proposes an IES model based on a demand response mechanism and a multi-time-scale optimization scheduling method with the aim of fully utilizing its flexibility. Specifically, the demand response strategy of electric heating/cold complementary substitution and horizontal time shifting was introduced at different stages of dispatch to enhance the flexible dispatching capability of the IES. Furthermore, the contribution of thermal inertia of pipes in resisting supply and demand uncertainties and reducing thermoelectric coupling was considered. On this basis, the time resolution was determined according to the adjustment rates of the different components, and a multi-timescale optimal scheduling method was developed. The quantum sparrow search algorithm was applied to solve the proposed model, and the optimal scheduling strategy of the system was obtained. Results revealed that, from an environmental perspective, the amount of wind and light abandonment was reduced by 89.71%, thereby increasing the penetration of renewable energy and promoting carbon neutrality. From an economic perspective, the daily operating cost decreased by 1380 yuan/day, effectively improving the operational costs of the IES. From a reliability perspective, thermal inertia of networks reduced the intensity of thermoelectric coupling and increased the flexibility of IES dispatching, curbing the uncertainties in renewable energy and loads during system operation.

Multi-Indicator Fused Resilience Assessment of Power Grids Considering Wind-Photovoltaic Output Uncertainty during Typhoon Disasters

Extreme weather events such as typhoons pose a serious threat to the safe operation of power grids. In the field of power system resilience assessment during typhoon disasters, a parametric typhoon wind field model combined with actual historical meteorological data has not been well adopted, and the conventional renewable energy uncertainty modeling methods are not suitable for typhoon disaster periods. In this paper, a multi-indicator fused resilience assessment strategy considering wind-photovoltaic uncertainty and component failure during typhoon disasters is proposed. Firstly, based on the actual historical meteorological data of typhoons, an uncertainty model of typhoon wind speed is established by a rolling non-parametric Dirichlet process Gaussian mixture model. Then, a spatial–temporal contingency set is constructed by considering the best-fit wind field model and stress–strength interference model for failure probability of transmission lines. On this basis, a holistic resilience assessment framework is established from the perspectives of priority, robustness, rapidity, and sustainability, and the entropy weight method combined with the technology for order preference by similarity to an ideal solution is leveraged to obtain the comprehensive resilience indicator. Finally, numerical studies are performed on the IEEE-30 bus test system to identify vulnerable lines and improve system resilience during typhoon disasters.

Optimal dispatch for cross-regional integrated energy system with renewable energy uncertainties: A unified spatial-temporal cooperative framework

Cross-regional energy sharing is a promising way to alleviate the imbalance in energy distribution. However, there is a risk that the uncertain fluctuations of intermittent renewable energy sources (RES) could be transmitted across regional transmissions, resulting in large-scale system economic inefficiency and security concerns. To this end, a unified spatial-temporal cooperative framework for the integrated energy system, which considers the coordination between intra-regional multi-energy coupling and cross-regional multi-agent-system energy sharing, is proposed in this paper. First, in the temporal dimension, a multi-time-scale stochastic decision-making model is established based on stochastic model predictive control (SMPC). Thus, slow-response units are arranged to gain economic goals in day-ahead stage and the closed-loop rolling adjustment is implemented on fast-response units to achieve accuracy supply-demand balance in real-time stage. Second, in the spatial dimension, a fully distributed dispatch approach is adopted to address the information sharing barriers, which only requires neighboring information and considers consistency of boundary control variables on border lines. Finally, to improve the computational efficiency, the established complex time sequential model is decomposed into a set of time-discrete parallel sub-problems, using the optimal condition decomposition (OCD) technique. Simulation results on a cross-regional test system demonstrate that proposed framework can increase the wind power consumption capacity and reduce operation cost through cross-regional cooperation, while the parallel decomposition can save 67.25 % of the computation time relative to the serial computation.