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Optimal planning of distributed generation (DG) in distribution networks is key for improving energy utilization efficiency and system operation benefits. Considering the uncertainties and temporal correlations of DG output and load demand, a multi-scenario chance-constrained economic model for DG planning is established in this paper. The model considers the comprehensive benefits of environmental, reliability and other aspects, as well as the active curtailment management of DGs. Thereafter, the model is extended to the coordinated planning of both DGs and tie lines, which is formulated as a multi-objective model. An improved genetic algorithm based solving strategy integrated with game multi-objective decision method is proposed. The feasibility and effectiveness of the proposed models are verified on the IEEE 33-bus distribution system. The impacts of natural resource distribution, confidence level, unit environmental cost and other parameters are investigated as well. The case studies also prove the integration of complementary DG generation could help improve the maximum capacity of DG and indicate the environmental benefit is the vital incentive to introduce DG into the distribution network. [ABSTRACT FROM AUTHOR]

The increasing integration of distributed resources, such as distributed generations (DGs), energy storage systems (ESSs), and flexible loads (FLs), has ushered in a new era for the active distribution system (ADS), characterized by more reliable, economical, and low-carbon. Nonetheless, with the increase in number and variety, how to realize self-consistent and self-optimal operation among these distributed resources has become major challenge for ADS. In this paper, a multi-agent deep reinforcement learning (MADRL) based algorithm with strategic goals of the real-time optimal scheduling of ADS is proposed, in which the uncertainty of renewable generations (RDGs), loads and electricity price are considered. The control variables contain the active and reactive power of dispatchable thermal DGs, the reactive power of photovoltaic and wind turbine DGs, the exchange power of ESSs, and the demand response (DR) of FLs. Besides, the region ownership of distributed resources is considered in our MADRL framework to resolve the partitioned optimization problem in large-scale ADS. Finally, the effectiveness and superiority of the proposed algorithm are demonstrated on the 33-node and 152-node active distribution system, including the terms of cost-effective and uncertainty adaptation. • A model-free optimal scheduling method for active distribution system is proposed. • Only the local measurements are required for agents in the Dec-POMDP formulation. • The designed reward function improves the strategy quality and learning efficiency. [ABSTRACT FROM AUTHOR]

The energy system carbon reduction is an inevitable trend to deal with the global warming problem. The power industry, being a vital part of energy system, faces severe challenges. To decarbonize the power sectors, the implementation of low-carbon technologies and integration of high penetrated renewable power generation would be an effective solution. Therefore, given the existence of multi-type low-carbon technologies including the flexibility reformation of coal-fired units, construction of gas-fired units and installation of energy storage systems, a low-carbon economic planning model of integrated electricity-gas systems with high penetration of wind generation is proposed. The carbon tax and carbon capture technology are integrated to accelerate carbon reductions. In particular, the strategic planning cases contributed by different scenarios are formulated. The simulation results on an electricity-gas test system demonstrate the feasibility and effectiveness of the proposed model in carbon reduction as well as wind curtailment decrease. Compared with the initial state, the implementations of the proposed three planning strategies can reduce carbon emissions by 9.8%, 32.5% and 9.3%, respectively. Meanwhile, the wind power curtailment ratio is decreased by 63.2%, 38.9%, and 63.7%, respectively. Moreover, a sensitivity analysis of carbon tax price and wind power penetration level are performed to investigate the low-carbon transition of the integrated electricity-gas systems. • A low-carbon economic planning model is established. • Sensitivity analysis reveals the applicability of proposed strategies. • The flexibility could be enhanced by the proposed planning strategies. [ABSTRACT FROM AUTHOR]

A reasonable dispatch strategy is of significance to meet and balance the operation economy, carbon emission and operation risk requirements for energy systems. A low-carbon economic dispatch model for electricity-gas systems is proposed, in which the carbon capture systems (CCS), carbon emission trading (CET), demand response (DR) and renewable generation are considered. The impacts of different low-carbon technologies on system economy and carbon emission are investigated. To manage the risk caused by uncertain renewable generation and CET price, the low-carbon economic dispatch problem is formulated as a risk-constrained two-stage stochastic dispatch model based on the chance-constrained programming (CCP). A predefined confidence level is introduced to control the loss of load risk and the transmission line overload risk in the proposed model. Case studies demonstrate the feasibility and effectiveness of the proposed models. • A risk-constrained low-carbon economic dispatch model is proposed. • The renewable generation and CET price uncertainty is considered. • A balanced trade-off between economy, carbon emission and operation risk is achieved. [ABSTRACT FROM AUTHOR]

The stochastic nature of wind speed hinders the forecasting of wind power generation. To improve the accuracy of wind power forecasting and effectively utilize the capability of principal component analysis (PCA) to process high-dimensional data, and take the advantages of deep belief network (DBN) to process complex data and spatial correlation (SC) in considering geographical position and terrain, a hybrid forecasting method using numerical weather prediction (NWP) is presented in this paper. First, an improved DBN is proposed by introducing Gaussian-Bernoulli restricted Boltzmann machine, and an adaptive learning step technique is applied to improve the convergence speed. Furthermore, the principal components are extracted from high-dimension original data by PCA, which are further input to the improved DBN. Then, a wind speed correction model is established to address the inaccuracy of NWP. Moreover, the output of the target site is forecasted using the data of its neighboring observation sites. Finally, the advantages of the above methods are combined, and the sliding window strategy is utilized to adaptively update the training data. The simulation results verify the effectiveness of the proposed improved DBN and the hybrid method compared to the traditional DBN, the corresponding average increase of forecasting accuracy is 15.8975% and 29.3725%, respectively. • An improved deep belief network (DBN) adding Gaussian function to RBM is designed. • The spatial correlation (SC) are used to consider geographical position and terrain. • An adaptive learning step technique is applied to improve convergence speed. • Sliding window strategy is utilized to adaptively update training data of forecasting method. [ABSTRACT FROM AUTHOR]

Large-scale distributed wind generation (DWG) integration brings new challenges to the optimal operation of the distribution network. The reactive supports from wind turbines (WTs) and reactive power resources can improve both the operation economy and renewable energy consumption. In this paper, a multi-period reactive coordinated optimal operation model for DWG in the distribution network is established. The active-reactive power coordination characteristics of two typical types of WTs are considered and the operating strategy of reactive power resources is integrated in the model. The second-order cone programming (SOCP) is developed to transform the original nonlinear power flow model into a linear and convex model, which would significantly improve the power flow calculation efficiency for DWG penetrated distribution network. The simulation results show that the integration of reactive power resources can further promote the consumption of DWG and improve the operating profits of the distribution network. • A multi-period optimal operation model for distributed wind generation is established. • Reactive supports from wind turbines and reactive power resources are investigated. • The second-order cone programming is integrated to optimize the whole benefits. [ABSTRACT FROM AUTHOR]

An energy-reserve co-optimization model for electricity and natural gas systems with multi-type reserve resources is proposed. Considering the reserve capacity provided by generating units is difficult to meet the large-scale regulation requirements caused by wind power, load demand forecast errors and unexpected contingencies, a reserve scheme with multi-type reserve resources including generating units, energy storage devices, and interruptible loads is designed. Besides, the transmission capability of reserve capacity in the electricity and gas networks when facing forecast errors and contingencies is considered in the proposed model. To protect information privacy of different energy systems, an improved alternating direction method of multipliers (ADMM) is adopted to solve the energy-reserve co-optimization model. The feasibility and effectiveness of the proposed model and method are verified on a modified IEEE 24-bus power system and 6-node natural gas systems. • A reserve scheme with multi-type reserve resources is proposed. • The deliverability of reserve capacity is considered. • An improved ADMM based distributed solution strategy is designed. [ABSTRACT FROM AUTHOR]

To maximize the benefits of multi-area cooperation and guarantee system reliability, a day-ahead scheduling model for multi-area integrated electricity and natural gas systems (IEGS) considering reserve optimization is proposed in this paper. The reserve scheduling is integrated into the multi-area optimal dispatch model to hedge against the risk caused by the uncertainties in electricity and gas systems. The reserve constraints in electricity and natural gas systems are modeled as probabilistic forms with a predefined confidence level based on chance-constrained programming (CCP). In order to protect the information privacy of each area, the original centralized energy-reserve co-optimization problems are decomposed into several area sub-problems that can be solved independently. Furthermore, the alternating direction method of multipliers (ADMM) with self-adaptive penalty parameters is adopted to solve the proposed model in a decentralized manner. The feasibility and effectiveness of the proposed models and methods are verified in a three-area IEGS. • An optimal scheduling model for multi-area IEGS is established. • A probabilistic reserve scheme to cope with uncertainties is modeled. • An ADMM based decentralized solution strategy is designed. [ABSTRACT FROM AUTHOR]

The power-gas-coupling can realize the cascade utilization of energy in the integrated energy system, which is conducive to improving the utilization of energy and reducing pollution gases emissions. With the installation of smart metering, two-way communication between suppliers and consumers is feasible, which enables the implementation of demand response. A generic optimal planning model is proposed to assess the economic and environmental benefits of the capacity allocation of the grid-connected integrated energy system considering both price-based demand response and incentive-based demand response respectively. The optimal planning problem is formulated as a mixed-integer linear programming model with the objective to minimize the total annual cost. The results from three configuration modes are compared in the case study, which illustrate the economic and environmental benefits from demand response. In addition, the impact of the sales capacity constraint on the grid and the fluctuation of electricity and gas prices on the planning of the integrated energy system are also extensively studied considering demand response. • A optimal planning model on the grid-connected integrated energy system is proposed. • A price-based demand response strategy is designed. • Comparative cost-benefit analysis is undertaken. • Impacts of the external parameters on the planning are investigated. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) play an important role in the future energy system. The large-scale adoption of moving EV load significantly accelerates the integration of transportation and distribution systems. The method to simulate the mobility and charging of a single or aggregated EVs is the key to analyze EVs' flexibility on the operation of distribution network. Considering the integrated impacts from both the transportation and power systems, and the uncertainty of user's driving behavior and charging intention, this paper proposes a spatial-temporal simulation method based on the vehicle-transportation-grid trajectory. The trajectory can not only describe the destination location and time like the trip chain, but also give the key information including the driving path in a whole travel process. The driving, parking, and charging are analyzed by the proposed spatial-temporal simulation method. It models the driving behavior based on statistical results and transportation systems, EV energy consumption pattern based on battery energy, and the charging demand based on the user's subjective intention at the coupled systems. Finally, a 30-node transportation system is developed and integrated with a 33-bus distribution network to illustrate the proposed method. Two typical days, "workday" and "holiday", are simulated and compared under different EV penetration levels (0%, 20%, 50% and 100%), different trip chain ratio (the ratio of 3-trip chains is 50%, 70%, 90%) to demonstrate the effectiveness of the spatial-temporal simulation method. • A vehicle-transportation-grid trajectory framework is proposed. • The uncertainty of driving, parking and charging is considered. • Spatial-temporal features are modeled in the charging load simulation. • Different EV penetration levels and trip chain ratios are simulated. [ABSTRACT FROM AUTHOR]

To achieve low-carbon operation optimization of integrated electricity gas network (IEGN) system with uncertainty, a two-stage stochastic decentralized low-carbon economic scheduling model and its solution method for multi-region interconnection is proposed. Firstly, an IEGN operation cost model considering the stepped carbon emission cost and the benefits of P2G's participation in carbon market transactions is constructed, considering demand-side management (DSM) with user satisfaction and degree of regulation. Secondly, multi-region interconnection is served to overcome single-region space-time limitations with the multi-energy flow, regional decoupling is realized by copying multi-region IEGN boundary nodes, and the scenario method is used to quantify the output uncertainty of IEGN. Hence, a two-stage stochastic optimal scheduling model of multi-region IEGN is established. Then, an improved successive linearization method is proposed for the non-convex transformation. Considering the decentralized autonomy and information privacy of different decision-makers, a decentralized optimization model is established, and the synchronous alternating direction multiplier method (synchronous-ADMM) based on nested improved successive linearization is introduced for the iterative solution. Finally, the feasibility and superiority of the proposed model and algorithm are verified by comparing and analyzing the simulation results of multi-region IEGN under different scenarios and scheduling strategies. • A cooperative low-carbon economic dispatching model for multi-region IEGN is proposed. • Resource uncertainty, DSM and the inter-regional collaboration capacity are considered. • The parallel S-ADMMA algorithm could improve the computational efficiency. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have received significant attention in recent years. The high penetration of EVs increased the charging load in the distribution network, which has an enormous impact on "transportation and power grid" coupled energy networks. This paper developed the models and methods to evaluate the capacity of electrical energy supply based on voltage stability in the worst charging case scenario, under the existing coupled network. An optimization method with transportation network constraints is proposed to find the worst charging case scenario. In addition, the mobility of EVs is considered to calculate the load margin and the maximum number of EVs charging that a given grid can support in the critical situation. The method and model are simulated by test cases, which provide a perspective for realizing the EVs integration capacity limitation in urban areas considering the coupling relationship between transportation and power grids. Finally, the impact of charging location and route choice in the voltage margin limitation is emphasized. • A model for generating the worst charging scenario is proposed. • A voltage stability analysis considering EVs' integration is proposed. • The transportation and power grid are coupled and modeled. • The allowable maximum number of EVs is calculated. [ABSTRACT FROM AUTHOR]