Day-ahead composite load forecasting for more informed load modelling and network operation

Secure and economical operation of distribution networks needs the management of reactive power resources. Optimal Reactive Power Dispatch (ORPD) optimally manages the reactive power scheduling of generators and distribution generations as well as the Reactive Power Compensation (RPC) devices. This paper investigates the effect of load models on the multi-objective ORPD problem in active distribution networks. Moreover, a modified Grey Wolf Optimizer (GWO), which is called in this paper as Civilized GWO (CGWO), is introduced to solve the ORPD problem. The proposed strategy including multi-objective function, various load models, DG’s reactive power, RPCs and the introduced CGWO, is tested on standard IEEE 33- and 69-bus distribution systems. The obtained results indicate the load models have significant impact on the cost function amount. Moreover, the performance of the proposed algorithm is evaluated using ten standard benchmark functions. The optimization results demonstrate the robustness of the introduced optimization algorithm and its ability in finding the better solutions compared to the Particle Swarm Optimization (PSO), Exchange Market Algorithm (EMA), and original GWO.

This paper presents a novel control design for D-STATCOM to ensure grid code-compatible performance of distributed wind generators. The approach considered in this paper is to find the smallest upper bound on the H∞ norm of the uncertain system and to design an optimal linear quadratic Gaussian (LQG) controller based on this bound. The change in the model due to variations of induction motor (IM) load compositions in the composite load is considered as an uncertain term in the design algorithm. The performance of the designed controller is demonstrated on a distribution test system representative of the Kumamoto area in Japan. It is found that the proposed controller enhances voltage stability of the distribution system under varying operating conditions.

Optimal distributed generators allocation with various load models under load growth using a meta-heuristic technique

Optimal power flow (OPF) is a crucial aspect of distribution network planning and operation. Conventional heuristic algorithms fail to meet the system requirements for speed and accuracy, while linearized OPF approaches are inadequate for distribution networks with high R/X ratios. To address these issues and cater to multi-period scenarios, this study proposes a dynamic linearized second-order cone programming-based (SOCP) OPF model. The model is built by first establishing a dynamic OPF model based on linearized second-order conic relaxation (LSOCR-DOPF). The components of the active distribution network, such as renewable energy power generation units, energy storage units, on-load-tap-changers, static var compensators, and capacitor banks, are then separately modeled. The model is implemented in MATLAB and solved by YALMIP and GUROBI. Finally, three representative scenarios are used to evaluate the model accuracy and effectiveness. The results show that the proposed LSOCR-DOPF model can ensure calculation time within 3 min, voltage stability, and error control within 10−6 for all three applications. This method has strong practical value in the fields of active distribution network day-ahead dispatch, accurate modeling of ZIP load, and real-time operation.

Increasing penetration of distributed energy resources is manifesting as voltage regulation challenges in many LV networks. Appropriate regulation of supply voltage magnitude is essential to ensure efficacy and efficiency in the operation of electricity supply networks and consumer equipment. While the theoretical impacts of supply voltage magnitude on the performance of consumer equipment, which include additional energy consumption and decreased equipment lifespan as voltage magnitude increases, are relatively well known, this has not been translated into quantitative impacts. This paper applies the outcomes of previous impact of supply voltage magnitude studies by the authors, in conjunction with domestic load models, to develop algorithms to estimate the quantitative impacts of supply voltage magnitude on consumers. The paper then applies these algorithms to calculate quantitative economic impacts that can be associated with the magnitude of the supply voltage. The outcomes of this research suggest that the per-annum impact of supply voltage magnitude on consumer equipment loss of life is potentially an order of magnitude greater than the resultant increased energy consumption based on case studies using Australian data.

The increasing use of distributed resources and flexible loads with fast active power regulation in the distribution network provides a new approach to power grid operation control. Addressing the challenge of real-time control of user-side loads, conservation voltage reduction (CVR) is introduced to directly manage the feeder load power. Firstly, the K-means clustering algorithm combined with KL divergence is used to analyze the distributed photovoltaic characteristics. Secondly, the feeder load power regulation characteristics are identified based on the load modeling method. Then, considering the active management measures of various regulating equipment for power and voltage, a multi-objective optimization model for the collaborative operation of the distribution network is established, using comprehensive operation cost and node voltage deviation rate as indicators. Finally, using the IEEE 33-node distribution network as an example, the results show that the proposed method demonstrates good energy-saving and voltage regulation performance.

Very sensitive loads require the safe operation of electrical distribution networks, including hospitals, nuclear and radiation installations, industries used by divers, etc. To address this issue, the provided paper suggests an innovative method for evaluating the appropriate allocation of Distribution STATic COMpensator (DSTATCOM) to alleviate total power losses, relieve voltage deviation, and lessen capital annual price in power distribution grids (PDGs). An innovative approach, known as the modified capuchin search algorithm (mCapSA), has been introduced for the first time, which is capable of addressing several issues regarding optimal DSTATCOM allocation. Furthermore, the analytic hierarchy process method approach is suggested to generate the most suitable weighting factors for the objective function. In order to verify the feasibility of the proposed mCapSA methodology and the performance of DSTATCOM, it has been tested on two standard buses, the 33-bus PDG and the 118-bus PDG, with a load modeling case study based on real measurements and analysis of the middle Egyptian power distribution grid. The proposed mCapSA technique's accuracy is evaluated by comparing it to other 7 recent optimization algorithms including the original CapSA. Furthermore, the Wilcoxon sign rank test is used to assess the significance of the results. Based on the simulation results, it has been demonstrated that optimal DSTATCOM allocation contributes greatly to the reduction of power loss, augmentation of the voltage profile, and reduction of total annual costs. As a result of optimized DSTATCOM allocation in PDGs, distribution-level uncertainties can also be reduced.

The use of power electronics technology to transform the distribution network, realize multi-terminal flexible closed-loop operation and greatly improve the power supply capacity is an important form of future development of the distribution network. At the same time, along with the large-scale promotion of electric vehicles (EVs), the disorderly access of charging load is bound to bring challenges to the safe and stable operation of the distribution network. Therefore, it is necessary to evaluate and analyse the impact of EV charging load access in the flexible distribution network. Firstly, this paper constructs operational constraints based on the operating characteristics of the flexible distribution network; secondly, based on the disorderly access characteristics of EVs, the EV charging load model is constructed through a multi-factor combination; finally, tests are carried out in the IEEE 33 interconnection system. The results show that the flexible interconnection can effectively optimize the distribution of tides over time and suppress the voltage deviation.

Under the conditions of scarcity of fossil energy and increasing environmental pollution, solar energy as an important renewable energy source has been emphasized by countries all over the world due to its cleanliness, large reserves, and sustainable use. With the increasing penetration rate of distributed photovoltaic, the distribution network has started to show new features such as diversified operation modes, bidirectional currents, and complex stabilization mechanisms, which brings new challenges to the safe and stable operation of the distribution network in the new situation. Therefore, this paper investigates the security evaluation and improvement measures of the distribution network under the background of high charging penetration rate. First, considering the uncertainty of photovoltaic (PV) output and load demand, a distribution network operation risk assessment method based on the improved put forward Monte Carlo sampling is proposed, and the low deviation sequence is improved by combining randomization and linear scrambling, while the improvement measures are studied based on the real-time tariff model and the demand response mechanism of the residential load model. Finally, the simulation analysis is carried out using the IEEE-34 node system. The results show that with the increasing penetration rate of distributed PV, the risk of node voltage overrun and the risk of partial branch current overrun also increase, and compared with the traditional method, the risk value obtained by the method proposed in this paper can reflect the real situation more accurately and stably.

Recent shift towards Renewable Energy Sources, backed by encouraging regulatory structure of Govt. and increased awareness regarding their clean energy, greatly affects power network’s operations due to intermittent behaviour of their sources. Be it wind, hydro or solar, all are intermittent in nature due to change in wind velocity, water discharge and radiation intensity respectively. It necessitates the pre-planning or day-ahead scheduling of the dispatch for optimal operation of the power network as a whole. Impact on congestion management needs to be observed on hourly basis for power system's stable operation. Current work examines the issue of congestion management in pool electricity market in presence of hourly varying wind power and seasonal load variation including 24 h load model for winter, spring, summer and fall seasons, while respecting line security limits and ensuring system voltage stability through maximization of loadability limits. Market clearance is done for the day-ahead market by submitting 3-block bid structure to the ISO. Linear curve modelling has been done for three block bid structure as a function of increment/decrement components of rescheduling. Impact of wind and seasonal load variations on congestion costs payable by ISO has been studied for IEEE 24-bus RTS system.KeywordsCongestion Management (CM)Wind Power Plants (WPPs)Generation reschedulingBlock bid functionSeasonal load

This paper investigates the impact of network structures on small-disturbance voltage stability of power systems in the long-term. A differential-algebraic model is established in terms of network topology characteristics and the aggregate nonlinear recovery load model. We define the weighted load connectivity (WLC) as an index to indicate the stability of network structures. Then robust stability assessment is used to eliminate the impact of uncertain recovery time of loads. The Monte Carlo method is used to study the relationship between WLC and small-disturbance voltage stability (SDVS) for a broad range of network topologies and parameters. We discover from the simulation results that the network with a larger value of WLC has a stronger ability to maintain SDVS. To further study the impact of renewable energy sources (RESs), we improve the system model by including an inverter-based distributed generator in parallel with each aggregated nonlinear dynamic load. The simulation results of the Hong Kong power grid show that the WLC can still indicate the stability of network structures effectively, and the systems with more stable network structures are less impacted by the penetration of RESs. The proposed WLC appears useful in providing guidance for power network planning and operation.

In this thesis, a transfer function-based load model is determined for commercial and industrial load. This model is derived from the composite load model which consist of an induction motor and static load. This developed model is compared to composite load model by considering two cases: 1) a small motor composition load or commercial load and 2) higher motor composition load or industrial load. The research is conducted through MATLAB/Simulink simulation. In order to compare the dynamic response of developed model, a comparative study has been done between the two models. In addition, the influence of voltage and frequency dependency terms on the overall model accuracy for developed model has been evaluated through several case studies considering both voltage and frequency dependency disturbances. A short-term load forecast model is developed for an electrically heated house. This research work is based on experimental data collected by installing current sensors in a house in St. Johns, Newfoundland, Canada. The data was collected for three years and only one-year data is used for this model. The model is based on Recurrent Neural Network (RNN) with wavelet transform. The proposed model is verified by comparing other developed models in the literature through MATLAB deep learning toolbox and wavelet toolbox. The proposed model can more accurately forecast the load.

A methodology for measurement-based load modeling based on integrating dynamic models is presented in this paper. A composite load model (CLM) and an exponential recovery load (ERL) model are combined and used as a model structure for load representation coupled with a heuristic criterion. An improved particle swarm optimization (IPSO) algorithm that considers a speed update modification to enhance its global search capabilities is used to identify the model's parameters. Several simulations are performed in two test systems, using load response measurements after small and severe disturbances, considering noise measurement to simulate monitoring devices. Results verify that the proposed load model has a better performance than the individual load models, and also IPSO algorithm is adequate for parameter identification.

In this paper, load modelling has been done in electrical distribution system using local real time test data. This distribution system supplies base loads, residential, industrial, commercial and composite loads. Using power and current-mismatch functions in polar form, a comprehensive framework for applying the Newton–Raphson method to solve power flow problems is presented. The Newton–Raphson approach for solving power flow problems can be applied in six different ways using these two mismatch functions. For load (PQ)buses and generator (PV) buses, we propose a theoretical framework for analyzing these versions. In addition, we compare newly created versions of the Newton power flow method to current variants in this study. Numerical studies on distribution networks are used to study the convergence behavior of all approaches. The measurements are formed for short term load forecasting with different types of realistic loads such as base loads, residential, industrial, commercial and composite loads. The long-term load forecasting and their losses also has been performed along with short term load forecasting. The results are obtained and validated through MATLAB.

The time-varying parameter identification of load models has attracted broadly attention when large amounts of intermittent distributed generations (DGs) and stochastic loads are integrated in active distribution network (ADN). However, in traditional time-varying parameter identification approaches, the load model is usually developed with the composite load model (CLM) or synthesis load model (SLM), which are not suitable for representing the dynamics of DGs. Moreover, the plateau phenomenon and the continuous low-quality data further reduce the performance of traditional load models with time-varying parameter. Therefore, to address these issues, a novel time-varying parameter identification approach with extended Kalman filter (EKF) is designed for the load modeling. To represent the behavior of modern loads, an improved load model contains a parallel SLM and voltage source converter (VSC) is developed. Then, the target parameters, whose changes produce larger variations in model outputs, are selected with trajectory sensitivity to avoid plateau phenomenon. Also, the Chi-square test and the proposed weighted suppression strategy are utilized to suppress the continuous low-quality data. The simulation results on a system-level ADN model show that the proposed approach could accurately identify the parameters of time-varying load models.