Optimal scheduling of active distribution network based on demand respond theory

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.

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.

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.

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.

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.

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.

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.

Trilayer stackelberg game scheduling of active distribution network based on microgrid group leasing shared energy storage

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.

After the micro-grid for electric heating supply is connected to the active distribution network (AND), the ADN and the micro-grid, as different interest subjects, are more challenging in economic dispatch, make the system run more economical and increase the rate of consumption of new energy. This paper, an optimization model of AND system considering the comprehensive demand response of electric heating load is proposed. First, the basic structure of micro-grid including electric heating load is explained, and the equipment model including gas turbine, heat storage equipment, thermoelectric conversion equipment and energy storage battery is buileded. Secondly, the optimal scheduling model of ADN based on price demand response is established, the target goal is reduce the system operation cost.In the end, the improved IEEE-33 node system is given to verify the feasibility of the proposed method and model is proved.

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.

The utilization of the electricity storage into batteries system is increasing. Most batteries are connected at the point in the network where the power of renewable energy sources is produced. These battery storage systems serve primarily to power electricity to households. They are most often used in cooperation with renewable energy sources, especially photovoltaic power plants. When installing renewable sources without using battery storage in distribution networks, there are several problems in the production of electricity from renewable sources. At a time of low power consumption and high electricity generation at the point of connection of renewable energy sources, a change in power flow occurs. The power generated from the sources flows into the distribution network or up to the superior network. If a distribution company installed battery storage at the point of connection, the power from renewable sources would not flow to the rest of the distribution system and would be stored on that distribution network node. Once the capacity size has been correctly determined at a given node, the distribution company could benefit from such solution. The distribution company could lease the capacity of the batteries to customers who have the renewable energy sources to store their produced energy or could cover part of the electricity consumption at the time of lack of production from the renewable energy sources. The paper deals with this issue, the location of battery storage is simulated on a standardized distribution network.

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

The distribution system operator (DSO) needs an optimal day-ahead scheduling (ODAS) for the economic and sustainable supply of electrical energy considering input parameters such as the price of the upstream grid. Next-generation distribution networks or smart distribution networks are the future networks where responsive loads are available. Renewable sources include wind turbines and solar panels have expanded. The presence of electric vehicles and the purchase from the electricity upstream market is provided, and the network can be programmed and controlled through the devices which record and transmit information. In this chapter, a robust optimization (RO) method has been proposed to minimize the cost of ODAS of smart distribution system (SDS) considering load-responsive and renewable energy sources (RESs) such as wind turbine (WT) and nonrenewable sources such as diesel generators (DGs) and battery energy storage system (BESS). The proposed method considers all the technical constraints of the upstream grid and DGs and the utilized BESS. In order to model SDSs, a 33-base IEEE test system has been used in the evaluation of the proposed model. The proposed ODAS concept determines the optimal level of exchange with the upstream network, the production of each dispersed generation unit, and the participation of demand response (DR) programs. It also provides an optimal layout for charging and discharging the BESS. It can be observed that the proposed model has the optimal scheduling capabilities of the SDSs considering the uncertainties of power market price. Moreover, it is observed that the resilient optimization method reduces the cost of network operation and confronts the price of electricity with uncertainty.

Chapter 4 - Data-driven robust stochastic optimization for power systems operations