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Under the background of the new power system, it is important to make full use of the distributed generation in the distribution network to achieve carbon peaking and carbon neutrality goals. With the wide access of distributed power sources, there is bi-directional power flow between the transmission and distribution networks. If the transmission and distribution networks still follow the traditional equivalence method for optimal power calculation, accurate results will not be obtained. Therefore, a transmission-distribution-network-coordinated optimal power flow model is established. The node tearing method is used to decouple the coupling variables of the transmission network and distribution networks. On this basis, the iterative form of the model with immobile points is obtained by mathematical derivation. The traditional heterogeneous decomposition algorithm only uses the latest calculation results for iterative computation. Hence, combining with the Anderson acceleration idea in mathematics, the heterogeneous decomposition algorithm based on Anderson acceleration is proposed by using the historical iteration values to optimize the iterations of immobile points. It is demonstrated by numerical experiments that, the established model can fully utilize the initiative of the distribution networks and reduce carbon emissions. The proposed algorithm has high accuracy. In addition, compared with the heterogeneous decomposition algorithm, its convergence performance is significantly improved.

Flow battery is an ideal choice for long-term and large-scale energy storage due to its advantages of numerous charge-discharge cycles, high capacity and long lifespan. However, the flow battery's complex structure poses high demands on battery control system, and traditional development methods are challenging to meet the flow battery system's diverse control requirements. To address this problem, a method for developing a flow battery control system based on event-driven technology is proposed, which aims to improve control precision and real-time performance. The proposed approach firstly presents a main-auxiliary stack synergistic architecture to address issues such as high stability requirements and large internal losses of flow batteries, and the architecture is modeled and analyzed. Then, the control system is designed by modularization based on event-driven technology, including the modules of flexible charging-discharging control, black start-up control strategy with auxiliary stacks, and state of charge (SOC) estimation based on Kalman filtering. Finally, a semi-physical simulation platform is built to validate the proposed architecture and strategies, which demonstrates that the proposed architecture and strategies can improve the energy conversion efficiency and stability of the system.

In traditional microgrid with droop control,the bus frequency decreases with the increase of load,and a corresponding frequency regulation control strategy is taken to keep frequency around the rated value. Control strategies based on proportional integral (PI) are commonly used to recover the bus frequency at present. Because of the possibility of changes in structure and system parameters of microgrid,the quick response and quick restoration of frequency may not be fulfilled with PI control. To solve this problem,the single neuron adaptive PI control method is used as frequency recovery algorithm in microgrid central controller in the second control layer to realize non-error control. In order to accelerate frequency recovery and improve the robustness of microgrid,the proportionality coefficient of the single neuron PI controller is optimized online by fuzzy controller. By comparing this method with original single neuron adaptive PI control with fixed neuron proportionality coefficient in simulation. It is proved that this modified method can improve transient performance of frequency recovery control and accelerate frequency recovery of microgrid.

In the context of intelligent power consumption, household users can promote the safe, stable and economic operation of the power system by participating in electricity demand response and obtain additional benefits. Therefore, a strategy for demand response based on family agglomeration is proposed in this paper. Firstly, the concept of family clustering is proposed to manage the aggregation of residents' families into communities and to design the response mechanism on the residents' side. Secondly, considering the residents' electricity consumption habits and influencing factors during demand response, the responding load function of household electricity consumption is constructed, which quantifies the potential of household demand response. On this basis, a response strategy is designed which takes both the degree of completion and user experience into account. Finally, three specific indicators are proposed to evaluate the response strategy, namely task completion rate, user discomfort and economic benefits. An example is given to verify the effectiveness and economy of the proposed strategy.

Micro-energy networks are the smallest element of integrated energy systems, and tapping into the integrated demand response potential of micro-energy networks is conducive to improving energy use efficiency and promoting the development of new energy sources on a large scale. This paper proposes a day-ahead integrated demand response strategy for micro-energy grid that takes into account the dispatchable loads. Considering the gradient use of thermal energy, a typical micro-energy grid structure including electricity, gas, medium-grade heat, low-grade heat, and cold energy is constructed, a comprehensive energy equipment model is established, and the refined scheduling models of the dispatchable loads are given. On this basis, with the operating economy of the micro-energy grid as the optimization objective, the integrated demand response strategies of tariff-type and incentive-type are proposed. Through case study analysis, it is verified that the proposed strategy can optimize the energy consumption structure of the micro-energy grid under the guidance of time-of-use tariffs, reducing the operating costs. The proposed strategy fully exploits the demand response potential of the micro-energy grid through the dispatchable loads and the multi-energy complementarity of electricity, heat, and cold, realizes the comprehensive coordination and optimization of source-network-load-storage, provides a larger peak-regulating capacity, and exhibits practical applicability in engineering. [ABSTRACT FROM AUTHOR]

Because of their renewable and non-polluting characteristics in power production, distributed photovoltaics have been developed, but they have also been criticized for the volatility of their output power. In this paper, an integrated energy system optimal dispatching model is proposed to improve the local absorption capacity of distributed photovoltaics. First, an integrated energy system consisting of electricity, heat, cooling, gas, and hydrogen is modeled, and a mathematical model of the system is constructed. After that, the uncertainty of distributed photovoltaic power and load demand is modeled, and a typical scenario data set is generated through Monte Carlo simulation and K-means clustering. Finally, an optimal dispatching model of the integrated energy system is constructed to minimize the daily operating cost, including energy consumption, equipment operation and maintenance, and curtailment penalty costs, as the optimization objective. In the objective, a segmented curtailment penalty cost is Introduced. Moreover, this paper presents a chance constraint to convert the optimization problem containing uncertain variables into a mixed integer linear programming problem, which can reduce the difficulty of the solution. The case shows that the proposed optimal dispatching model can improve the ability of photovoltaics to be accommodated locally. At the same time, due to the introduction of the segmented curtailment penalty cost, the system improves the absorption of distributed photovoltaic generation at peak tariff intervals and enhances the economy of system operation. [ABSTRACT FROM AUTHOR]

An iteration-variable-correction (IVC) based method is proposed for solving optimal power flow (OPF) of transmission-distribution-coupled (TDC) systems. First, a TDC system can be divided into a single coordinator and multiple agents. Then, the basic formulation, subproblem construction, and selection of iteration variables are given based on the conventional heterogeneous decomposition algorithm (HGD). Considering the limitations of the conventional HGD, the IVC is proposed. According to the general theory of the IVC, when it is applied to solve TDC OPF, the voltage magnitude of boundary buses and the multipliers corresponding to boundary power injections will be corrected every four iterations with first-order and second-order differential operations on historical results in the previous four iterations. The quadratic convergence of the IVC is proved under several weak assumptions. The numerical experiments demonstrate that the proposed IVC has the same accuracy as the method based on the centralized OPF model. Also, better convergence and efficiency can be achieved under the IVC compared with some conventional decomposition methods, including alternating direction method of multipliers, auxiliary problem principle method, analytical target cascading method, and the HGD. [ABSTRACT FROM AUTHOR]

Considering that the conventional synchronous splitting (MSS) method for power flow calculation of coupled transmission and active distribution (TDC) systems has limitations in efficiency and convergence due to the penetration of distributed generations (DGs) and loop operation, this paper proposes an asynchronous forward-backward-splitting power flow algorithm of TDC systems. In this algorithm, the transmission system operator (TSO) acts as a coordinator and processes forward steps while the distribution system operators (DSOs) act as agents and process backward steps. Two first-in-first-out queues are introduced to realize an asynchronous mechanism, and thus, the TSO does not have to wait for the slowest DSO to complete its power flow calculation and power injection update before the next iteration can proceed. This asynchronous mechanism has a higher efficiency compared with a synchronous one when communication delays exist. In addition, the conventional fixed-point iteration in the MSS method is replaced with the Krasnosel’skii-Mann iteration. The proposed algorithm with appropriate settings will achieve better convergence, especially when active distribution networks are considered, where volt-walt-control-typed and volt-var-control-typed DGs are penetrated. The linear convergence of the proposed algorithm is proved under several assumptions and the parameter tuning issue is discussed with numerical experiments. [ABSTRACT FROM AUTHOR]

The ever-increasing integration of variable wind energy requests for a power system with high flexibility. In this paper, we formulate the real-time economic dispatch problem as a multi-stage robust program to leverage flexible resources, especially energy storage systems, in a broader timescale, and also to avoid the risk of high costs in critical scenarios. The multi-stage robust program is decomposed to the dynamic programming form and the fast robust dual dynamic programming (FRDDP) algorithm is developed to solve it efficiently. To tackle the nonconvexities caused by the inter-temporal constraints of ESSs, an estimation-correction method is developed, and measures of the approximation accuracy are presented. Comprehensive case studies comprising units up to 1371 are carried out to verify the effectiveness of the proposed model. Numerical results show that compared with the existing ones, the proposed multi-stage robust model achieves lower cost under the worst-case scenario and shows better scalability in large-scale systems. [ABSTRACT FROM AUTHOR]

This paper introduces a novel successive-intersection-approximation-based power flow method for integrated transmission and distribution networks (I-T&D). In this method, the entire I-T&D is split into the transmission network part, distribution network part, and boundary part. Then, alternate iterations are processed between a transmission network (TN) and several distribution networks (DNs) by exchanging the voltage and power injection of boundary buses. Particularly, the voltage magnitude of the boundary buses will be modified every two iterations by obtaining an intersection with the results in the previous two iterations. The local quadratic convergence of the method is proved strictly. The numerical experiment demonstrates that the proposed method has the same accuracy as the method based on the global model. Also, better convergence and efficiency can be achieved under the proposed method compared with the master-slave-splitting method in several different circumstances, DNs with loops, DNs with distributed generations, DNs with on-load tap changers, large-scale systems, etc. Besides, the proposed method is applied in the contingency analysis, achieving satisfying convergence and efficiency. [ABSTRACT FROM AUTHOR]

Considering the increasing coupling between transmission and distribution networks, an affine arithmetic-based interval power flow algorithm of integrated transmission and distribution (I-T&D) networks is proposed. First, a novel two-step approximation method for sine and cosine functions is proposed to reduce the number of noise variables in the affine form of the injected power, as the basis of the proposed algorithm. Then, this algorithm is divided into four stages, which are implemented alternatively between a transmission system operator (TSO) and multiple distribution system operators (DSOs) by exchanging parameters at TSO-DSO boundaries. Three-phase unbalanced I-T&D networks and boundary tap changer action are also discussed. In addition, an asynchronous power flow method is proposed to mitigate the influences of communication networks on the proposed interval power flow algorithm. Numerical experiments demonstrate the effectiveness of the proposed two-step approximation method, as well as the conservative property, fairly good approximation effect, high efficiency, and low data exchange amount of the proposed algorithm in various circumstances, including single-phase and three-phase networks, varying degrees of unbalance, various smart inverter controls, etc. [ABSTRACT FROM AUTHOR]

An asynchronous heterogeneous decomposition (async‐HGD) algorithm for integrated transmission and distribution networks coordinated optimisation considering communication conditions is proposed. Compared with traditional synchronous HGD (sync‐HGD), a partial synchronisation mechanism is introduced instead of the full synchronisation to alleviate the influence of communication delays. Under this mechanism, the transmission system operator does not need to wait for the slowest distribution system operator to complete its subproblem and update boundary parameters before the next iteration can proceed. Also, a bounded delay is introduced under this mechanism to ensure sufficient freshness of all the updates. Further, the previous value strategy (PVS) and linear regression strategy (LVS) are applied to handle non‐instantaneous interruptions in the communication networks as an improvement of the async‐HGD. Numerical experiments show that considering communication delays in the actual operation; the async‐HGD takes less time to converge compared with the sync‐HGD under appropriate settings. In addition, PVS and LVS can both enhance the robustness of the overall algorithm by achieving approximate solutions, and the LVS can achieve more accurate results compared with the PVS. [ABSTRACT FROM AUTHOR]

As the coupling between transmission systems and distribution systems is significantly enhanced, transmission–distribution coordinated optimisation becomes more and more necessary. Considering that the objectives of the transmission system operator (TSO) and the distribution system operator (DSO) are usually significantly different in the type and dimension, a synthesised‐objective collaborative model is proposed in this study, by normalising different objectives, introducing a weight factor to reflect the importance degree difference of multiple objectives and constructing a common objective. Then, a modified heterogeneous decomposition (M‐HGD) algorithm is proposed to solve the model. In the M‐HGD, two modifications on the update of iterative variables and the stop criteria are proposed, which effectively relieves the fluctuation of the iterative variables and restrains the tail effect of the convergence curve. The proposed model and algorithm can avoid privacy issues between the TSO and the DSO, achieve Pareto optimality solutions, and have powerful flexibility. Numerical experiments validate the effectiveness of the proposed model and demonstrate that the M‐HGD has high accuracy and has better convergence and efficiency than traditional distributed optimisation methods. [ABSTRACT FROM AUTHOR]

To satisfy the robust and real-time requirements of power flow calculation for large-scale power systems, a globally convergent method is proposed with trust-region techniques, which shows satisfying robustness and convergence. Then, this method is combined with Newton's method to achieve a two-stage algorithm, benefiting from their different advantages. In the first stage, the proposed globally convergent method is used for searching power flow solution. When the values of state variables in a certain iteration are close enough to the real operational point, the algorithm enters the second stage to use Newton's method to achieve the solution. This two-stage algorithm can achieve an accurate solution for solvable cases, and can also achieve a least-square solution, which is an approximate solution for unsolvable cases. Numerical experiments show that the proposed globally convergent method and two-stage algorithm have better robustness and efficiency than the existing methods in previous research. They have universality for well- and ill-conditioned systems as well as the cases under ill-conditioned operational modes, heavy loads and inappropriate initial values. They can also handle different limit violations, such as reactive power limit in PV buses and voltage limit with tap changers action, which could significantly benefit real practice. [ABSTRACT FROM AUTHOR]

This paper proposes a new problem formulation to simplify the mathematical representation for a hybrid ac/dc domestic energy system. Instead of building the formulation from a component level, the new formulation is built at the whole system level such that all power transfers between ac/dc or dc/ac are reflected in the ac power drawn from the main grid. This is achieved by assigning the ac power drawn from the grid as a piecewise function of local dc power, each slope in the function represents the conversion efficiency, making it flexible to consider a wide range of conversion efficiencies for different system components. The new formulation substantially reduces the number of variables/constraints, improves efficiency modelling accuracy and increases the search efficacy. The piecewise functions are directly solved by a mixed integer linear programming (MILP). The performance of the proposed formulation is illustrated by the hybrid ac/dc energy systems at a primary school in the U.K. The results show that the proposed problem formulation and the MILP solution method provide an effective optimal control strategy for a mixed ac/dc domestic energy system in the presence of variable tariffs and differing conversion efficiency, achieved a cost saving of 24% increase in energy bills compared with the traditional approach. [ABSTRACT FROM AUTHOR]

This paper discusses how fast-response distributed battery energy storage could be used to implement post-contingency corrective control actions. Immediately after a contingency, the injections of distributed batteries could be adjusted to alleviate overloads and reduce flows below their short-term emergency rating. This ensures that the post-contingency system remains stable until the operator has redispatched the generation. Implementing this form of corrective control would allow operators to take advantage of the difference between the short- and long-term ratings of the lines and would therefore increase the available transmission capacity. This problem is formulated as a two-stage, enhanced security-constrained OPF problem, in which the first-stage optimizes the pre-contingency generation dispatch, while the second-stage minimizes the corrective actions for each contingency. Case studies based on a six-bus test system and on the RTS 96 demonstrate that the proposed method provides effective corrective actions and can guarantee operational reliability and economy. [ABSTRACT FROM PUBLISHER]

The well‐known sequential Monte Carlo simulation is a powerful tool to analyse short‐term reliability of complicated composite power system. However, the corresponding computational burden is tremendous when applied to a highly reliable system. Aiming at improving the simulation efficiency, an adaptive importance sampling technology is proposed in this study. The proposed method, on the basis of component state duration sampling technique combined with cross‐entropy method, is able to provide reliability evaluation of highly reliable non‐homogeneous Markov system. The Weibull and lognormal distributions are considered to describe repair time of individual components comprising a system. Through the case studies conducted on a reinforced Roy Billinton reliability test system, it is validated that the proposed method is effective and of high efficiency and the efficiency gain against the crude sequential Monte Carlo simulation is robust to variation of load level and lead timescale. The proposed method is useful and efficient to timely monitor the system operating pressure under different lead timescales. [ABSTRACT FROM AUTHOR]

In this paper, the concept of normal measurement rate (NMR) is defined based on the theory of uncertainty in measurement and a robust state estimator named maximum normal measurement rate (MNMR) estimator is proposed. Comparison is made between the MNMR estimator and other robust estimators. The robustness and precision of this estimator is tested with the IEEE 14-bus, 30-bus, and 118-bus systems. Simulation results show that the MNMR estimator is effective in identifying bad data and is also time efficient. [ABSTRACT FROM PUBLISHER]

• Asynchronous mechanism accelerates transmission-distribution coordinated analysis. • Asynchronous algorithm can effectively handle with communication delays. • Bi-directional network equivalence satisfies accuracy requirements. • Bi-directional network equivalence can effectively handle with interruptions. • Convergence is stable when limit violations and tap changers action are considered. This paper presents an asynchronous distributed global power flow method for transmission-distribution coordinated analysis. Considering the communication delays between the transmission system operator (TSO) and distribution system operators (DSOs), this paper introduces 'partial synchronization' instead of full synchronization in the traditional synchronous method. Under this mechanism, the TSO needs not to wait for the slowest DSO to complete its power flow calculation and power injection update before the next iteration can proceed. Numerical experiments demonstrate that the proposed asynchronous method takes less time compared with the synchronous method under the same accuracy requirement. Further, this paper proposes a bi-directional network equivalence method to work against non-instantaneous interruptions. Polynomial fitting is used to describe the characteristics of networks, which enables transmission and distribution networks can independently finish power flow calculation when a non-instantaneous interruption happens. Numerical experiments demonstrate that the proposed method can achieve high accuracy. [ABSTRACT FROM AUTHOR]

An analytical method based on generalized polynomial chaos (gPC) is proposed for probabilistic load flow (PLF). The method preserves the nonlinearity of power flow equations whose rectangular formulations are adopted to facilitate the gPC expansion. The feasibility of the method is demonstrated by case studies from a 9-bus system. [ABSTRACT FROM AUTHOR]

Previous work has shown that robust state estimator based on maximum normal measurement rate is accurate and reliable. However, in the existing algorithm, the optimal solutions cannot be obtained due to the approximation of evaluation function. Besides, there are no noise-filtering effects on normal measurements. To deal with these two problems, this letter presents an improved algorithm to get a more accurate solution with higher normal measurement rate. Numerical tests on different systems show the proposed algorithm is efficient. [ABSTRACT FROM PUBLISHER]