Collaborative Optimization Method of Active Distribution Network Based on Conservation Voltage Reduction

Several studies and field tests have shown the potential of conservation voltage reduction (CVR) to decrease peak demand, energy consumption, and system losses. Nonetheless, the energy savings achievable by CVR is dependent on the load model, which could be constant power, current, impedance, or any combination of these models. Also, the use of distributed energy resources (DERs) at the distribution level presents a challenge to successful CVR implementation as these resources could cause voltage fluctuations in the system. This paper studies the impact of load models on CVR implementation in a distribution system feeder interfaced with renewable DERs. Load flow analyses are carried out using constant power (PQ) and constant impedance (Z) load models, respectively, and the resulting effects on feeder losses and energy consumption are observed as CVR is implemented. In addition, the impacts of DERs and voltage regulation devices on the feeder voltage profile are analyzed. The feeder losses showed opposite trends with constant PQ and Z loads as CVR is implemented. Overall, the results showed varied impacts of load models and DER output on feeder losses, energy consumption, and bus voltages in the distribution network.

Detailed load monitoring is critical for real-time Volt/Var Control (VVC) in distribution systems. Estimating the load behavior becomes even more critical in one of the common forms of VVC - Conservation Voltage Reduction (CVR). In this paper, a load monitoring and modeling method is proposed to support real-time VVC. A recursive estimation method is used to identify the parameter of the proposed load model in real-time. The proposed load model and its estimation are tested on IEEE 13 node system with detailed load modeling of 629 houses. Simulation results show that the recursive estimation method tracks the measurements well and performs better than least square method with a 5 min real-time control step.

The conservation voltage reduction (CVR) technique is a well-established technique for peak demand reduction and energy savings in the presence of voltage-sensitive loads. However, intermittent and uncertain nature of renewable energy sources such as photovoltaic generation may produce some operational issues under real-time CVR operation, which results in major problems such as undervoltage as well as increase in power losses in an active distribution network. Advanced power-electronics-based devices such as inverter-based distributed generators (IDGs) and soft open point (SOP) devices emerge as potential solutions for the aforementioned issues by providing the volt-ampere reactive (VAR) support. However, an efficient CVR methodology is much needed for proper coordination of multiple traditional volt/VAR control devices and advanced power-electronics-based devices. Therefore, this article introduces a multistage coordinated CVR methodology incorporating IDG and SOP devices for offline (i.e., day-ahead scheduling) and online (i.e., real-time dispatch) studies. For day-ahead scheduling, stochastic CVR formulation has been developed considering intermittency and uncertainties in power loads and generations. In contrast, online simulations have been performed in two stages for near-real-time operation using an inner-day rolling control model based on model-predictive control and real-time control through modified volt/VAR droop (VVD) control by optimal VVD curve selection. To validate the proposed scheme, a real-time cosimulation platform using a real-time digital simulator and a MATLAB-GAMS interface has been built. The efficacy of the proposed method has been validated on various case studies and also tested on sudden external disturbances such as sudden cloud transient, cloud cover, and substation voltage changes. Furthermore, the robustness of the proposed method has also been checked under faulty conditions. The test results demonstrate the significance of the proposed methodology on voltage violation mitigation, energy loss, energy consumption minimization, and service restoration.

Distribution networks have been traditionally designed with limited monitoring and controllability. Therefore, most Conservation Voltage Reduction (CVR) schemes have been implemented in MV networks. However, the deployment of on-load tap changers (OLTCs) and monitoring in LV networks creates the opportunity to actively manage voltages closer to end customers, unlocking further benefits. This work proposes an active CVR control scheme that, using limited monitoring, manages voltages in OLTC-enabled LV networks. A stochastic quantification process that caters for load uncertainties is used to assess the active control's annual benefits for different control cycles, considering realistic time-varying household profiles and models per day type and season (l-min resolution). Results on a real UK residential LV network model demonstrate the inadequacy of the well-known CVR factor to assess advanced control schemes. More importantly, the proposed approach significantly increases annual energy savings compared to fixed or active primary substation voltage reductions, regardless the control cycle.

This paper aims to investigate Community Energy Storage (CES) impacts on AMI-based Volt-VAR Optimization (VVO) solutions for advanced distribution networks. CES is one of the technologies employed to improve system stability, reliability and quality. As such, it could have considerable impacts on voltage control, reactive power optimization and energy conservation. Conservation Voltage Reduction (CVR) is one of the main tasks of advanced VVO engines in distribution networks. Moreover, in order to check the performance of the discussed VVO engine in the presence of CES during peak time intervals, 33-node distribution feeder is employed. The results of this paper show significant improvement in the performance of the VVO engine when CES is forced to discharge in peak times. Moreover, the results present how CES could affect Volt-VAR Control Component (VVCC) switching and how it affects the energy conservation efficiency.

The development of Smart Grid technologies and the need to defer investments for expanding and reinforcing distribution networks have attracted new attention to aspects of Conservation Voltage Reduction (CVR). Based on the fact that the demand of certain loads can change with voltage, it is plausible to envisage the active management of voltage regulation devices in order to increase/decrease the demand during certain periods for the benefit of the distribution network (e.g., peak shaving) or the whole system (e.g., fast reserves). The extent of this voltage-driven demand response is however entirely dependent on the instantaneous load composition. In order to quantify the benefits of such a scheme, this work proposes a methodology to estimate the time-varying residential load response to primary substation voltage changes whilst complying with voltage limits at low voltage. This is applied to a small UK distribution network downstream an OLTC-enabled 33kV/6.6kV substation with 351 residential, 2 commercial and 1 industrial customers (total peak demand of 932 kW). The results, only considering part of the residential loads modeled with time-varying ZIP parameters, suggest that - even considering voltage constraints- there is considerable load response that can be unlocked from residential loads. However, this is highly dependent on the time of the day.

This paper establishes a distributed distributionally robust optimization model of the AC/DC distribution network driven by historical data. Firstly, this paper analyzes the historical data and uses Copula theory to establish the joint probability distribution of wind power error and forecast power, then obtains the conditional probability distribution of error under certain forecast power. Secondly, this paper decouples the AC/DC hybrid distribution network into AC subnetwork and DC subnetwork, and takes the minimum comprehensive operating cost of each as the optimization goal, among which the carbon trading mechanism is introduced in the AC subnet optimization, and the distributed optimization model of AC-DC distribution network is established. Thirdly, in view of the uncertainty of wind power, an ambiguous set based on KL divergence is constructed. This paper takes the aforementioned error probability distribution as a reference distribution, and the proposed model is converted into a single-layer linear optimization target by using Lagrange dualism theory, and then the distributed solution is performed by using the alternating direction multiplier method. Simulation experiments are carried out on the modified 33-nodes AC/DC distribution network system model, and the results show that the proposed model can effectively reduce the carbon emissions on the distribution network side and significantly improve the consumption capacity of wind power.

Distribution network tariff design: Facilitate flexible resource under uncertain future energy scenarios

Control mode of distribution power grid dispatching was studied and frame of ‘Generation-Grid-Load-Energy storage’ (GGLE) coordinating control system was built. This system took controlling resources into regulative scope such as distributed energy source, energy storage system and flexible load. On this basis, coordinated optimizing control strategy such as multi-source information fusion, feeder level load forecasting and operation control of distribution network was designed, thus enhancing the safe and reliable operation of active distribution network and diversifying the ability of load to participate in peak load regulation of power grid, which contributed to the reasonable allocation and consumption of distributed power supply. The GGLE coordinated control system has been successfully constructed in a 10kV switching station on Ninghai Avenue in Qinhuangdao, realizing a reasonable control of energy and load, and improving of the energy efficiency and power distribution reliability. The pilot project could be a basic guidance for large-scale construction of active distribution grid.

In this paper, we present an optimal operation model for an active distribution utility with inflexible and flexible loads. The proposed model addresses how the flexible loads should be operated to minimize the utility operation cost as well as maintaining the distribution network safe. Moreover, we analyze how the flexible loads can provide the reserve service for the electricity market with highly renewable resources penetration. Assuming T time intervals, we determine the optimal energy and reserve bids to the electricity market that minimize energy procurement cost of the utility and fully comply with the operational standards of the distribution network. From a set of numerical experiments on the 33-bus system, we present the advantages of our proposed model.

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

The integration of distributed generators (DGs) into distribution networks is one of the main issues facing distribution networks. This paper presented a detailed analysis of the impacts of the renewable DGs allocation on system steady state and transient performance of distributed networks. An oscillatory particle swarm optimization (OPSO) algorithm was used to find the optimal locations and sizes of DGs for achieving the objectives of minimizing total transmission losses, system voltage regulation or real power performance as a single- and multi-objective optimization problem in distribution networks. The effectiveness of the method is demonstrated with the IEEE14 bus as a distribution network with 50% increase in system loading considering several penetration scenarios to evaluate concentrated and distributed DGs. Also, the impact of DGs penetration on system transient performance was obtained using detailed non-linear models of both synchronous machines and DGs sources (Photovoltaic and wind) when the system was subjected to a symmetrical three-phase short circuit fault for 6 cycles. The obtained results will help in planning the system with high penetration levels of renewable DG sources and identifying the critical penetration levels for the studied system.

A flexible load active management method in optimization operation of distribution networks is proposed to discuss the significant role of active management of flexible load in regulating load curve. The models of translatable load, shiftable load and curtailable load are established and they are divided into price incentive flexible load and contract incentive flexible load firstly. Secondly, a multi-objective optimal dispatch model considering electricity purchasing cost, load peak valley difference and users' acceptance is established for different types of flexible load. Then, considering the proportion of different types of flexible load in different user types, the flexible load response model of residential load, industrial load and commercial load are obtained. Finally, the regulation effects of load curve and dispatch strategies of the overall flexible load are obtained by summarizing the dispatch strategies of different user types.

The emerging distribution networks are equipped with renewable generation resources, controllable loads and corresponding monitoring and control assets to actively regulate the aggregated demand in the bulk power system. The volume of the energy received from the bulk power system as well as the price of electricity contributes to the operation cost of the distribution network. Distribution system operators balance the demand and supply and compensate for the intermittency and variability of the local renewable generation resources and noncontrollable loads using the controllable generation and demand assets. Data centers are large flexible electrical loads in distribution networks that could help the distribution system operators to manage the operation cost and to mitigate the imbalance between the demand and generation. The introduced uncertainties in the operation horizon provide economic risks in the distribution network operation. Hence, providing efficient measures for risk associated with the uncertainties provides an insight for the decision makers to avoid over conservative operation decisions. The proposed risk averse operation planning framework constrains the volatility of the expected cost through the conditional value at risk (CVaR) assessment.

With the large-scale integration of renewable energy generation and flexible loads, the source-load matching in power grids has become increasingly challenging, necessitating the full exploration of flexible load regulation potential. To conduct quantitative assessment of adjustability capacity for multiple types of flexible loads, this paper first defines six standard indicators for evaluating flexible load adjustability: maximum adjustable capacity, adjustable duration, response time, regulation rate, regulation gradient, and regulation quality. Subsequently, response models are established for various types of flexible loads based on their characteristics, including thermostatically controlled loads, distributed energy storage, electric vehicles, adjustable industrial loads, data centers, and 5G base stations, with detailed descriptions of power constraints, dynamic constraints, and other characteristic constraints for each load type. The models are then established based on standard indicators to quantitatively assess the adjustability of different flexible loads. Finally, a distribution network optimization scheduling model with objectives of maximizing renewable energy accommodation and minimizing system operation cost is constructed and tested in the IEEE 33-bus test system. Results demonstrate that the proposed method can effectively quantify and assess the regulation capacity of flexible loads, and verify the important role of diversified flexible loads in distribution network.