Multi-Objective Optimization Scheduling of Active Distribution Network Considering Large-Scale Electric Vehicles Based on NSGAII-NDAX Algorithm

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 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.

This paper proposes a multi-objective benefit function for operation of active distribution systems considering demand response program (DRP) and energy storage system (ESS). In the active distribution system, active network management (ANM) is applied so that the distribution system equipment is controlled in real-time status based on the real-time measurements of system parameters (voltages and currents). The multi-objective optimization problem is solved using ε-constraint method, and a fuzzy satisfying approach has been employed to select the best compromise solution. Two different objective functions are considered as follows: benefit maximization of distribution company (DisCo); benefit maximization of distributed generation owner (DGO). To increase the benefits and efficient implementation of distributed generation (DG), DGO has installed battery as energy storage system (ESS) in parallel with DG unit. Consequently, DGO decides for the battery charging/discharging. DisCo has the ability to exchange energy with the upstream network and DGO. Also, DisCo focuses to study the effect of demand response program (DRP) on total benefit function and consequently its influence on the load profile has been discussed. This model is successfully applied to a 33-bus radial distribution network.

An active distribution network comprises renewable generation sources like solar and wind. Also, an active distribution network feeds electrical loads and plug-in hybrid electric vehicles. An optimal active and reactive power management strategy is imperative for a technically feasible and financially viable operation. The renewable sources are interfaced via power electronic voltage source converters, which can control the reactive power injection into the network. In this paper, a joint optimal active and reactive power control strategy is proposed, which utilizes the reactive power handling capability of the voltage source converters. Uncertainties of renewable generation, load demand, grid energy price, and charging load demand of the plug-in hybrid electric vehicles are modeled and incorporated using the probabilistic "Hong's point estimate method". A group of measures like optimal procurement of active and reactive power from available resources, optimal battery energy storage scheduling, demand response, smart transformer scheduling, and network reconfiguration are carried out to maximize the expected profit of the distribution network operator. The proposed method is validated by simulation studies on a thirty-three bus active distribution network.

With increasing connection of distributed generations (DGs), the power flow in electric distribution network is no longer unidirectional and the network has become active distribution network. Thus, the connection of DGs in electric distribution networks has created a challenge for distribution network operators due to bidirectional power flow. One of the main technical challenges of an active distribution network is to maintain an acceptable voltage level in the system. This has initiated efforts in using various voltage control strategies to regulate the network voltage profile so that the voltage is maintained at its allowable voltage limits. A number of voltage control methods have been applied to solve voltage control problems associated with the connection of DGs in a distribution system. These voltage control strategies may be classified as decentralized or distributed and centralized or coordinated voltage control. From the literature, a number of coordinated voltage control strategies have been implemented to provide better and faster control to the system. This chapter presents an improved coordinated voltage control method in active electric distribution network by coordinating the three voltage control methods, namely, power factor control, on load tap changer control and generation curtailment control. These voltage control methods are coordinated using fuzzy logic by considering the load bus voltages and DG power as inputs and the voltage control actions as the outputs. The fuzzy logic if-then control rules which are generated to build the fuzzy logic control system are based on the simulations results as well as from the previous works. Results obtained using the fuzzy logic based coordinated voltage control has shown that the voltages are able to be kept within its permissible limits.

Deployment of Electric Vehicles (EV) is increasing in recent years due to economic and environmental advantages compared with fossil fuel-based vehicles. As the market of EVs grows, new challenges to the electric grid are emerging to accommodate the EVs demand, especially in the distribution networks. In this paper, we investigate the impact of EVs deployment on the electricity demand and distributed network. We propose a model to generate EV demand profiles that consider the EV users’ driving pattern such as daily energy consumption and charging schedule, in addition to the EV’s charging characteristics. The EV demand model uses data we obtained from a survey to evaluate the model’s parameters. We use the EV demand model to simulate and evaluate the impact of EVs demand on the distribution network. We present a case study with an actual model for a distribution network to evaluate the impact of EVs on the distribution network in Saudi Arabia. We analyze the simulation results and show how EVs impact the demand and the distribution network performance.

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.

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.

Background: Due to the influence of illumination resources, the output of photovoltaic power generation exhibits intermittency, randomness, and volatility. The large-scale integration of photovoltaic power into the distribution network can cause fluctuations in bus voltage and line flow and, in severe cases, may lead to system voltage and flow limits being exceeded, increasing the risk of system operation. Methods: In response to this, this study proposes an active distribution network flow optimization method based on energy storage regulation. This method takes the total system operation cost, which includes elements such as the cost of photovoltaic power generation, network loss cost, energy storage regulation cost, and curtailment cost, as the optimization objective. It uses the bus voltage, line flow, and transformer load of the active distribution network as the constraint for accommodating space. Additionally, it considers the charging and discharging regulation capacity and sensitivity index of distributed energy storage. Based on this, a flow optimization model for the active distribution network is established, which is driven by the joint cost and sensitivity of the minimum total adjustment amount and the minimum number of control nodes. The model is solved using the path-tracking method. Results: In this paper, the improved IEEE39-bus system is used as an example for analysis, verifying the correctness and effectiveness of the proposed method. Conclusion: Research shows that this method can fully consider the technical and cost factors of high-proportion distributed photovoltaic grid integration. By optimizing the power flow of the distribution network through coordinated control of distributed energy storage, the proposed method achieves the minimum control adjustment amount while minimizing the number of nodes involved in the adjustment. This ensures the safe, reliable, and economical operation of the system.

Dynamic behaviors of distribution networks are of great importance for the power system analysis. Nowadays, due to the integration of the renewable energy generation, energy storage, plug-in electric vehicles, and distribution networks turn from passive systems to active ones. Hence, the dynamic behaviors of active distribution networks (ADNs) are much more complex than the traditional ones. The research interests how to establish an accurate model of ADNs in modern power systems are drawing a great deal of attention. In this paper, motivated by the similarities between power system differential algebraic equations and the forward calculation flows of recurrent neural networks (RNNs), a long short-term memory (LSTM) RNN-based equivalent model is proposed to accurately represent the ADNs. First, the adoption reasons of the proposed LSTM RNN-based equivalent model are explained, and its advantages are analyzed from the mathematical point of view. Then, the accuracy and generalization performance of the proposed model is evaluated using the IEEE 39-Bus New England system integrated with ADNs in the study cases. It reveals that the proposed LSTM RNN-based equivalent model has a generalization capability to capture the dynamic behaviors of ADNs with high accuracy.

In order to deal with probabilistic and possibilistic input uncertainties of distributed energy resources (DERs) of active distribution network (ADN) in optimal scheduling, a two-stage uncertain optimal scheduling model considering operational risk is proposed in this paper. Objectives of two-stage are the minimum operational scheduling cost and the lowest operational risk according to predicted values and practical values of DERs respectively. D-S theory based uncertain power flow calculation is inserted to the second-stage problem to estimate the load loss risk and prime-dual interior point method is adopted as the model solution. Simulations are made in improved IEEE 33-bus system and results validate the viability of the model and algorithms proposed to offer decision assistant supports to the uncertain optimal scheduling of ADN.

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 access of distributed generation has grant influences on the power distribution system network structure and as a consequence, changes its failure risk. This article presents a power failure risk evaluation model of power distribution network, which including two aspects of device level and feeder level. In the first place, the risk of the power distribution network containing distributed generation will be evaluated from two aspects, device failure possibility and device failure severity. Basing on varies devices' evaluation, through the comprehensive evaluation method to quantify the feeder's risk of failure. When the risk exceeds the threshold value, the system would start its safety precaution. Numerical example results show that, the evaluation method that this article established can realize the risk evaluation and early warning of the power distribution network containing distributed generation, the results have guiding significance for construction retrofitting, and hastens the optimal operation for active power distribution network.

This paper mainly investigates risk assessment of voltage limit violation in active distribution network with integration of wind generation (WG), photovoltaic generation (PVG) and electric vehicles (EVs). Firstly, to avoid additional peak load caused by random EV charging, a controlled EV charging and discharging strategy is designed. Then, for the correlations of spatially near WGs and PVGs, Nataf transformation and orthogonal transformation (OT) are integrated to solve the problem, and this provides a path for point estimate method (PEM) based probabilistic load flow (PLF) to obtain steady-state voltage of active distribution network. Furthermore, based on the voltage results, a model for quantifying the risk of voltage limit violation is developed by considering loss of load caused by voltage limit violation, which is different from the previous risk indices calculated by possibility and severity of voltage limit violation. Finally, the proposed model is tested on the modified IEEE 33-bus system. Simulation results confirm that the effective EV charging/discharging strategies and penetration increment of WG and PVG help to decrease operation risk of active distribution network.