Power System Parameters Forecasting Using Hilbert-Huang Transform and Machine Learning

A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to rank the importance of variables employed in the forecasting models. The Mean Decrease Gini index is employed as an impurity function. The resulting hybrid forecasting models employ the radial basis function neural network and support vector regression. Apart from introduction and references the paper is organized as follows. The section 2 presents the background and the review of several approaches for short-term forecasting of power system parameters. In the third section a hybrid machine learning-based algorithm using Hilbert-Huang transform is developed for short-term forecasting of power system parameters. Fourth section describes the decision tree learning algorithms used for the issue of variables importance. Finally in section six the experimental results in the following electric power problems are presented: active power flow forecasting, electricity price forecasting and for the wind speed and direction forecasting

Application of Integrated Hardware and Software System “PRIZNAK-10M” for Measuring Power-Frequency Electric and Magnetic Fields in Electric Networks

One of the urgent problems in modern power indus¬try is to provide safety of man staying in the affected area of elec¬tric and magnetic fields generated by operating electric facilities. The paper presents integrated hardware and software system "PRIZNAK-10M", intended for measurement of power-frequency electric and magnetic fields in electric networks. Inte-grated hardware and software system put into practice the me-thods of measurements, met the regulatory documents effected in electric networks of Russia. The recommendations have been de-veloped to provide the man safely at the facilities studie

Effect of Energy Storages on Flexibility and Cyber Resilience of Electric Networks

When planning and managing the present-day and future transformed electric power systems (EPS), such comparatively new properties as flexibility and cyber resilience shall be taken into account along with EPS conventional properties, such as Reliability, Security, Survivability, and Vulnerability. Large-scale introduction of renewable energy sources notably lowers the EPS flexibility. Installation of Energy Storages allows compensation of power production imbalance occurred when using renewable energy sources, which makes the energy system more robust, but lowers its cyber security. The paper considers the main performances and models of Energy Storages, their impact on flexibility and cyber security of electric networks; it also presents the technique for quantifying the flexibility of a network with Energy Storages, and identifies most promising directions of studies in this area

Improving the principles of short-term electric load forecasting of the Irkutsk region

Forecasting of electric load (EL) is an important task for both electric power entities and large consumers of electricity [1]. Large consumers are faced with the need to compose applications for the planned volume of EL, and the deviation of subsequent real consumption from previously announced leads to the appearance of penalties from the wholesale market. In turn, electricity producers are interested in forecasting the demand for electricity for prompt response to its fluctuations and for the purpose of optimal infrastructure development. The most difficult and urgent task is the hourly forecasting of EL, which is extremely important for the successful solution of problems of optimization of generating capacities, minimization of power losses, dispatching control, security assessment of power supply, etc. Ultimately, such forecasts allow optimizing the cash costs for electricity and fuel or water consumption during generation. This paper analyzes the experience of the branch of JSC "SO UPS" Irkutsk Regional Dispatch Office of the procedure for short-term forecasting of the EL of the Irkutsk region

Improving the principles of short-term electric load forecasting of the Irkutsk region

Forecasting of electric load (EL) is an important task for both electric power entities and large consumers of electricity [1]. Large consumers are faced with the need to compose applications for the planned volume of EL, and the deviation of subsequent real consumption from previously announced leads to the appearance of penalties from the wholesale market. In turn, electricity producers are interested in forecasting the demand for electricity for prompt response to its fluctuations and for the purpose of optimal infrastructure development. The most difficult and urgent task is the hourly forecasting of EL, which is extremely important for the successful solution of problems of optimization of generating capacities, minimization of power losses, dispatching control, security assessment of power supply, etc. Ultimately, such forecasts allow optimizing the cash costs for electricity and fuel or water consumption during generation. This paper analyzes the experience of the branch of JSC "SO UPS" Irkutsk Regional Dispatch Office of the procedure for short-term forecasting of the EL of the Irkutsk region

Development of Digital Twin for Load Center on the Example of Distribution Network of an Urban District

The paper proposes a concept of building a digital twin based on the reinforcement learning method. This concept allows implementing an accurate digital model of an electrical network with bidirectional automatic data exchange, used for modeling, optimization, and control. The core of such a model is an agent (potential digital twin). The agent, while constantly interacting with a physical object (electrical grid), searches for an optimal strategy for active network management, which involves short-term strategies capable of controlling the power supplied by generators and/ or consumed by the load to avoid overload or voltage problems. Such an agent can verify its training with the initial default policy, which can be considered as a teacher’s advice. The effectiveness of this approach is demonstrated on a test 77-node scheme and a real 17-node network diagram of the Akademgorodok microdistrict (Irkutsk) according to the data from smart electricity meters

Siting and sizing of wind farms taking into account stochastic nature of generation

The article deals with the problem of the negative impact of wind farms stochastic generation on power grid. One of the ways to reduce the stochasticity of the wind farms generation is their geographically distributed siting. A technique for sizing and distributed siting of wind farms from the standpoint of the influence on the variability of the total generated power is proposed. Modeling of wind power generation with hourly detailing is carried out using the developed Wind-MCA software based on data from archives of long-term observations of ground-based weather stations. The optimal distribution of wind turbines in potential locations is based on a genetic algorithm. The objective function is the coefficient of variation of the power generated by all wind farms in the sites under consideration, depending on the number of wind turbines in their composition. The genetic algorithm is implemented using the built-in MATLAB function. The proposed technique is applied to assess the capacity options and sites for wind farms in the Zabaykalsky Krai. The solution providing the minimum value of the coefficient of variation of the wind farms generated power and high value of the wind farms capacity utilization factor has been obtained

Management of Voltage Flexibility from Inverter-Based Distributed Generation Using Multi-Agent Reinforcement Learning

The increase in the use of converter-interfaced generators (CIGs) in today’s electrical grids will require these generators both to supply power and participate in voltage control and provision of grid stability. At the same time, new possibilities of secondary QU droop control in power grids with a large proportion of CIGs (PV panels, wind generators, micro-turbines, fuel cells, and others) open new ways for DSO to increase energy flexibility and maximize hosting capacity. This study extends the existing secondary QU droop control models to enhance the efficiency of CIG integration into electrical networks. The paper presents an approach to decentralized control of secondary voltage through converters based on a multi-agent reinforcement learning (MARL) algorithm. A procedure is also proposed for analyzing hosting capacity and voltage flexibility in a power grid in terms of secondary voltage control. The effectiveness of the proposed static MARL control is demonstrated by the example of a modified IEEE 34-bus test feeder containing CIGs. Experiments have shown that the decentralized approach at issue is effective in stabilizing nodal voltage and preventing overcurrent in lines under various heavy load conditions often caused by active power injections from CIGs themselves and power exchange processes within the TSO/DSO market interaction

Management of Voltage Flexibility from Inverter-Based Distributed Generation Using Multi-Agent Reinforcement Learning

The increase in the use of converter-interfaced generators (CIGs) in today’s electrical grids will require these generators both to supply power and participate in voltage control and provision of grid stability. At the same time, new possibilities of secondary QU droop control in power grids with a large proportion of CIGs (PV panels, wind generators, micro-turbines, fuel cells, and others) open new ways for DSO to increase energy flexibility and maximize hosting capacity. This study extends the existing secondary QU droop control models to enhance the efficiency of CIG integration into electrical networks. The paper presents an approach to decentralized control of secondary voltage through converters based on a multi-agent reinforcement learning (MARL) algorithm. A procedure is also proposed for analyzing hosting capacity and voltage flexibility in a power grid in terms of secondary voltage control. The effectiveness of the proposed static MARL control is demonstrated by the example of a modified IEEE 34-bus test feeder containing CIGs. Experiments have shown that the decentralized approach at issue is effective in stabilizing nodal voltage and preventing overcurrent in lines under various heavy load conditions often caused by active power injections from CIGs themselves and power exchange processes within the TSO/DSO market interaction