Two-stage uncertain optimal scheduling of active distribution network considering operational risk

The constraints in the active - reactive power coordinated optimal scheduling of the conventional active distribution network are single - faceted, the scheduling efficiency is low, resulting in an increase in the final network loss ratio. Therefore, a method for designing and analyzing the active - reactive power coordinated optimal scheduling in the active distribution network with consideration of demand response is proposed. In light of the existing scheduling requirements, an analysis of the active - reactive power scheduling requirements is initially conducted, and the adaptive approach is adopted to improve the overall scheduling efficiency and construct the adaptive balance scheduling constraints. Based on this, a demand - response - based active - reactive power coordinated optimal scheduling model for the active distribution network is established, and optimization and iteration are used to achieve optimal scheduling processing. The test results indicate that, in comparison with the contract - coordinated distribution network active - reactive power coordinated optimal scheduling method and the multi - level scale congested distribution network method, the test network loss ratios of the demand - response - based distribution network active - reactive power coordinated optimal scheduling method are kept below 2.5. This demonstrates that the active - reactive power coordination optimization scheduling method for the active distribution network is more practical and reliable, with better practical application effect and high scheduling efficiency, and has practical application value.

With increased uncertainties and depressed security margins of distribution networks, the risk of violating load or voltage constraints has increased. The usual risk-prevention method is to set an appropriate optimal schedule of active distribution networks to reserve enough margin without incurring extra operating costs. In this study, an optimal scheduling method for an active distribution network is proposed that allows for a low level of violation risk to minimize operating costs while maintaining good system security. Overload violation risk cost is a financial cost due to the cost of countermeasures needed to avoid overload violation. In this study, the N-0 security regions of overload are set as the constraint that provides the definition and calculation of the bidirectional violation distance of overloading for active distribution networks based on the N-0 security region. The relationship between the violation distance of overloading and the countermeasures against overload violation have been established based on the power flow equations, enabling the calculation of the countermeasures cost. The assessment model for the overload violation risk cost is derived from the cost of countermeasures and probability distributions of nodal power. The point estimation algorithm is introduced to simplify the calculation. The calculation of risk cost is fixed through the conditional value-at-risk. On this basis, a novel optimization scheduling model of active distribution networks that considers risk cost is constructed. Examples of the optimal schedule are designed and the validity of the proposed method is verified.

In this paper we present an adaptive robust optimization framework for the day-ahead scheduling of Active Distribution Networks (ADNs) where the controlled devices are distributed Energy Storage Systems (ESSs). First, the targeted problem is formulated using a two-stage optimization approach. The first-stage decisions determine the amount of import/export energy from the external grid at each hour, as well as energy exchanges for each ESS. The second stage deals with the intra-day control, given the first stage decisions. In order to effectively consider the impacts of uncertainties, the problem is transformed into a `min-max-min' formulation. Here, we minimize the first (day-ahead scheduling) and second stage (intra-day operation) costs while uncertainties are maximally affecting the cost function of the second stage. The Benders dual cut algorithm is employed for the solution of the optimization problem. IEEE 34 bus standard network is the benchmark grid for assessing the performances and effectiveness of the developed robust optimization process.

As the integration of distributed energy resources (DERs) accelerates, optimizing active distribution networks (ADNs) for low-carbon operations has become essential for decarbonizing power systems. This paper proposes a bi-level optimization model aimed at achieving low-carbon scheduling of ADNs interacting with multiple technical virtual power plants (TVPPs) in the integrated electricity and carbon markets. The upper-level problem seeks to minimize ADN costs, including expenses associated with energy procurement and carbon emissions. The lower-level problem maximizes VPP economic benefits by optimizing their power generation, demand response, and trading strategies. To enhance carbon accountability, the dynamic emission factors (DEFs) are developed, encompassing both generation-based and trading-based emission factors. The generation-based DEF quantifies emissions based on the output of distributed generators, while the trading-based DEF allocates emissions for ADNVPP power exchanges, preventing double-counting and ensuring accurate carbon cost assessments. The bi-level model is then reformulated as a single-level mixed-integer linear programming (MILP) problem using Karush–Kuhn–Tucker (KKT) optimality conditions, Fortuny-Amat transformation, McCormick Envelop method, and the strong duality theorem. Case studies conducted on a modified IEEE 33-bus test system demonstrate that the proposed approach can effectively reduce carbon emissions by 7.93% and further reduce total operational cost considering carbon price, achieving both economic and environmental objectives. These findings underscore the potential of integrating low-carbon strategies into ADN management for more sustainable and resilient power systems.

With the rapid growth of electric vehicle (EV) ownership, the problem of increased peak loads on distribution networks due to large-scale EV integration needs to be addressed. This study proposes an active distribution network multi-objective optimization scheduling method. It takes into account the charging demands of large-scale EVs and aims to minimize distribution network operating costs, reduce net load variance, and maximize the photovoltaic (PV) consumption rate. First, the Monte Carlo sampling method is used to analyze the charging load demands of a large number of EVs. Next, we construct a multi-objective optimization scheduling model for the active distribution network. This model integrates the charging demands of EVs with the operating constraints of the distribution network. To tackle the multi-objective optimization problem, we propose the NSGAII-NDAX algorithm. Additionally, we employ a fuzzy theory-based method to select the Pareto optimal solution set, addressing the challenge of decision-making complexity posed by the large size and information-rich content of the optimal solution set. Finally, the effectiveness of the proposed method in the comparative analysis of multiple scenarios is verified by an improved IEEE 33-node example. The experimental results show that the proposed model and method can effectively utilize EV charging load optimization to reduce the peak-to-valley difference in the system load while ensuring the system’s economic operation and the maximum PV consumption rate. Compared with the other algorithm, the NSGAII-NDAX algorithm has stronger optimization ability and can better handle multi-objective optimization problems.

Protection schemes are essential in active distribution networks and microgrids’ reliable, efficient, and flexible operation. However, the protection of these networks presents significant challenges due to operational changes, such as variations in topology, distributed energy resources connection/disconnection, and microgrid operating modes, among others. This paper proposes an adaptive protection scheme based on overcurrent devices with several setting groups based on artificial intelligence algorithms. The developed strategy is composed of two stages. In the off-line stage, a clustering technique is employed to group the active distribution network operating scenarios exhibiting similarities. The optimal settings for the protection devices are determined for each set of scenarios. On the other hand, in the on-line stage, the protection strategy’s implementation and operation, considering the active distribution network’s existing communication system, are defined. Furthermore, the approach formulates the overcurrent relay coordination as a mixed-integer non-linear optimization problem, and as a result, the optimal setting of the overcurrent protection devices is obtained. It aims to minimize the operating time, considering the transformers’ thermal limits, fuse operating curves, and overcurrent relay settings. The solution is determined by using an Augmented Lagrangian genetic algorithm. The presented protection scheme is validated on the modified IEEE 34 node test feeder, considering the main operating scenarios of the active distribution networks, such as topology changes, distributed energy resource connection/disconnection, and microgrid operating modes (on-grid and off-grid). The results obtained and its easy implementation indicates the high potential for real-life applications.

In order to meet the high permeability and the access of large scale distributed generation, at present and in the future, active distribution network becomes the trend of smart grid research. This paper introduces the influence of distribution network voltage distribution and stability of distributed energy access based on active distribution network; The reactive power and voltage optimization control method of active power distribution network is summarized from the aspects of optimal voltage regulation measure, voltage regulation measure of power network equipment, reactive power optimal dispatch of power system, and so on. Analysis of the current status of the research, and summarized the existing active distribution network with a distributed power pickup distribution network reactive power and voltage control of the existence of some problems and the insufficiency still some problems that must be solved in the study of reactive power and voltage optimization control of active distribution network are pointed out.

Active Distribution Networks are Multi-Input Multi-Output (MIMO) systems with coupled dynamics, which cause interactions among the control loops of Distributed Energy Resources (DERs). This undesired effect leads to performance degradation of voltage control. To mitigate the effects of this unavoidable coupling, the present paper proposes a systematic design procedure based on the analysis of the interaction’s sources. In detail, each DER is equipped with a double-loop PI to control the active and reactive power output of the voltage source converter, which connects the DER to the network’s node. Furthermore, to guarantee ancillary services, the two loops are coupled by a simple mechanism of cooperation of the active power to voltage regulation realized by a filtered droop law. To achieve voltage regulation with reduced loop interactions, the PI parameters and the filter’s pulse are designed according to a procedure with two sequential steps based on the Internal Model Control (IMC) technique. Simulation studies are finally presented to demonstrate that the proposed design method achieves both reduction of the loop interaction and robust voltage control in the presence of model parameter uncertainty in the MIMO plant, modeling various operating conditions of the ADN, including a step connection of large loads, renewable energy source variations, and changes in the substation transformer ratio.

With the gradual increase of distributed energy penetration, the traditional optimization model of distribution network can no longer guarantee the stable and efficient operation of the distribution network. In order to deal with the inevitable uncertainty of distributed energy, a new robust optimal operation method is proposed for active distribution network (ADN) based on the minimum confidence interval of distributed energy Beta distribution in this paper. First, an ADN model is established with second-order cone to include the energy storage device, capacitor bank, static var compensator, on-load tap changer, wind turbine and photovoltaic. Then, the historical data of related distributed energy are analyzed and described by the probability density function, and the minimum confidence interval is obtained by interval searching. Furthermore, via taking this minimum confidence interval as the uncertain interval, a less conservative two-stage robust optimization model is established and solved for ADN. The simulation results for the IEEE 33-bus distribution network have verified that the proposed method can realize a more stable and efficient operation of the distribution network compared with the traditional robust optimization method.

In recent years, the penetration rate of intermittent distributed generation (DG) in active distribution networks has been steadily increasing, leading to prominent issues such as voltage violations and network congestion. Coordinated operation of soft open points (SOPs) and distributed energy storage systems (DESSs) enable flexible resource management in both spatial and temporal dimensions, allowing real-time voltage regulation and flow control. This enhances the controllability, sustainability, and economic efficiency of the distribution network, ultimately improving user satisfaction. The optimization of this coordinated configuration has become a central challenge in research. Taking the different characteristics of DESSs and SOPs into account, this paper proposes a coordinated configuration method by introducing local marginal price (LMP) under the configuration scheme. The framework is modeled as a three-level problem, including planning and operation levels. Initially, typical scenarios are generated to address the uncertainty of distributed generation operation. At the upper level, the DESS configuration is optimized to minimize annual operational costs, while at the middle level, SOPs are planned based on the annual comprehensive operational cost of the distribution network. At the lower level, the objective is set as social welfare maximization to reflect user satisfaction by incorporating LMP as a planning indicator. It is then transformed and solved as a mixed-integer second-order programming model with a hybrid optimization algorithm. The model is established with the modified IEEE 33-node distribution system as a case study to validate the feasibility of the proposed configuration method. The case study results demonstrate the effectiveness of the proposed approach in optimally configuring SOPs with DESSs to reduce overall annual operating costs and enhance the economic efficiency of the system.

As natural disasters bring about power outage and financial losses, network resiliency is an important challenge for distribution network operators (DNOs). On the other side, power loss reduction during normal operating condition is a major concern of DNOs. In this paper, optimal scheduling of active distribution network (ADN) is addressed through simultaneous minimization of power loss in normal condition and load shedding in critical condition after natural disasters. A new formulation is developed for the network reconfiguration to optimize the system operation in both normal and emergency conditions in the presence of conventional and renewable-energy-based distributed generation (DG) as well as energy storage systems (ESSs). The line flow based (LFB) algorithm is used for the AC power flow calculations, and all the developed relations have been convexified to construct a mixed-integer quadratically-constrained programming (MIQCP) optimization model. The simulations have been implemented on the IEEE 33-bus system in GAMS, and the results are investigated.

The availability of sufficient and economic online capacity to support the network while encountering disturbances and failures leading to supply and demand imbalance has a crucial role in today distribution networks with high share of Distributed Energy Resources (DERs), especially Renewable Energy Resources (RESs). This paper proposes a two-stage decision making framework for the Distribution Management System (DMS) to flexibly optimize the day-ahead schedule of DERs and market participation of distribution networks under uncertainties, imposed by DGs outage and wind generators. The uncertainties are modeled via scenarios and convolved with each other, and then the joint scenario set is applied in the proposed two-stage programming model. Also the role of network constraints on DMS decisions are seen via a linearized AC power flow model and finally the resulted proposed framework is based on mixed-integer-linear programming (MILP) layout solved by CPLEX 12.6. To examine the effectiveness of the proposed framework, it is used for decision making of DERs scheduling and market participation strategy of a test distribution network.

Distributed energy resource (DER) including wind power, solar energy and energy storage system (ESS) are connected to the active distribution network (ADN) in various combination ways, which makes the distribution network have interaction. As a bridge connecting the transmission grid (TG) and micro grid (MG), ADN breaks the traditional operation pattern of TG+ADN+MG. Considering the physical connections and shared information among TG, ADN and MG, this paper proposes a decentralized and parallel analytical target cascading (ATC) algorithm for TG+ADN+MG multi-level co-optimization dispatch considering primary and secondary frequency regulation. To explore the synergistic ability of the TG+ADN+MG coping with uncertainties of DER, i.e., wind power, the primary and secondary frequency regulation of TG are implemented to cope with uncertainties. Furthermore, the distributional uncertainties of wind power and load are well modeled by data driven, which is proposed in our previous work [1]. An improved 6-bus system is used to test the proposed model, the numerical results illustrate the effectiveness and efficiency of the proposed method.

This paper aims to study the impact of demand side management (DSM) technology on the operational risk of active distribution network (ADN). Firstly, a demand side integrated management model is established including energy-efficiency load optimization and interruptible load scheduling, and the active management of load resources is completed. Then the severity indicator system is established, and an ADN risk assessment method based on improved Monte Carlo simulation (IMCS) method is proposed. Finally, a risk assessment of a regional power grid is carried out. The results show that DSM can significantly improve the operational risk indicators and ensure the reliable operation of ADN.

A new voltage sensitivity-based distributed feedback online optimization for voltage control in active distribution networks