Analysis of Electric Energy Consumption Profiles Using a Machine Learning Approach: A Paraguayan Case Study

Correctly defining and grouping electrical feeders is of great importance for electrical system operators. In this paper, we compare two different clustering techniques, K-means and hierarchical agglomerative clustering, applied to real data from the east region of Paraguay. The raw data were pre-processed, resulting in four data sets, namely, (i) a weekly feeder demand, (ii) a monthly feeder demand, (iii) a statistical feature set extracted from the original data and (iv) a seasonal and daily consumption feature set obtained considering the characteristics of the Paraguayan load curve. Considering the four data sets, two clustering algorithms, two distance metrics and five linkage criteria a total of 36 models with the Silhouette, Davies–Bouldin and Calinski–Harabasz index scores was assessed. The K-means algorithms with the seasonal feature data sets showed the best performance considering the Silhouette, Calinski–Harabasz and Davies–Bouldin validation index scores with a configuration of six clusters.

Increasing the Efficiency of Recuperation Through the Use of Energy Storage Systems for the Own Needs of Traction Substations

The use of regenerative braking by electric rolling stock on DC railways makes it possible to increase the energy efficiency of the transportation process. The effective use of regenerative braking is associated with creation of conditions for receiving energy obtained through it. For these purposes, rectifier-inverter converters and energy absorbing devices are currently used in the traction power supply system.A promising technology that provides an increase in theefficiency of the use of regenerative braking is energy storage, which allows this energy to be used in the future to cover the traction load curve. A feature of the use of regenerative braking on singletrack sections of DC railways with low traffic intensity is the need to use converters or energy absorbing devices. One of the options for increasing the efficiency of recuperation energy use is the adoption of energy storage systems for the own needs of traction substations. The use of this technical solution is advisable on single-track sections with intensive use of regenerative braking, the effectiveness of which is explained through a decrease in power consumption for own needs of the substation from the external grid.The international research allows us to identify the widespread trend towards the application of electricity storage technology in various fields: from renewable energy sources to electric power systems, including transport power supply systems. International practices demonstrate successful implementation of pilot projects of adoption of energy storage systems for solving problems of increasing the efficiency of electric urban and suburban transport, as well as of metro systems.The objective of the work is to assess the energy performance of energy storage systems when using recovered energy for own needs of a traction substation. The study is based on the methods of mathematical and simulation modelling, optimisation, and mathematical statistics.The discussed issues refer to the use of energy storage systems to provide power supply for own needs of DC traction substations. Main issues of operation of storage systems are considered with the help of a substation case study. The features of the recuperation load curve are described to explain the use of hybrid technologies for developing a storage system. The example of the considered traction substation helps to demonstrate the solution to the problem of determining main parameters of the storage system, considering the specifics of operation of electrochemical and electrical modules.

An Incentive-Based Implementation of Demand Side Management in Power Systems

The growing demand for electricity runs counter to European-level goals, which include activities aimed at sustainable development and environmental protection. In this context, efficient consumption of electricity attracts much research interest nowadays. One environment friendly solution to meet increased demand lies in the deployment of Renewable Energy Sources (RES) in the network and in mobilizing the active participation of consumers in reducing the peak of demand, thus smoothing the overall load curve. This paper addresses the issue of efficient and economical use of electricity from the Demand Side Management (DSM) perspective and presents an implementation of a fully-parameterized and explicitly constrained incentive-based demand response program The program uses the Particle Swarm Optimization algorithm and demonstrates the potential advantages of integrating RES while supporting two-way communication between energy production and consumption and two-way power exchange between the main grid and the RES.

Bottom-Up Model of Random Daily Electrical Load Curve for Office Building

In the design stage of energy systems in buildings, accurate load boundary conditions are the key to achieving energy supply and demand balance. Compared with the building cold and heat load, the generation of building electrical load has stronger randomness, and the current standard electrical load calculation method cannot reflect this feature. Therefore, this paper proposes a bottom-up high time resolution power load generation method for office buildings. Firstly, the non-homogeneous Markov chain is used to establish the random mobility model of personnel in office buildings, and the building electrical appliances are divided into four categories according to the different driving modes of personnel to electrical appliances in office buildings. Then, based on the personnel mobility model, the correlation between the use of electrical appliances in office buildings and the personnel in the room is established to construct the random power simulation model of different types of electrical appliances. Finally, the electric load of different types of electrical appliances is superimposed hourly to generate a random daily load curve. In order to verify the validity of the model, an office building is simulated and compared with the measured electrical load value. The verification results show that the model well reflects the daily distribution characteristics of electric load. The simulation value and the measured value are used for statistical analysis. The evaluation results show that the correlation between the simulation value and the measured value is high, which further illustrates the validity and accuracy of the model.

Research on Classification of Daily Load Curve of Distribution Network Based on Improved SSA-FCM

In the analysis of three-phase unbalance in distribution network, the accuracy of daily load curve classification results determines the size of three-phase unbalance. Aiming at the shortcomings of Fuzzy C-Means (FCM), a fuzzy C-Means clustering algorithm (SSA-FCM) optimized based on Sparrow Search Algorithm (SSA) is proposed. The cluster validity evaluation index is introduced to get the optimal quantity of clusters, and the SSA is used to search for the initial cluster center, which solves the problem that the FCM algorithm relies on the initial value and is easy to converge to local optimal solution. The simulation results show that, compared with the FCM algorithm, the load curves classified into the same category by SSA-FCM are closer together.

An Integer Non-Cooperative Game Approach for the Transactive Control of Thermal Appliances in Energy Communities

Non-cooperative scheduling games can be used to coordinate residential loads in order to achieve a common goal while accounting for individual consumer’s interests, privacy, and autonomy. However, a significant portion of the residential flexibility—Thermostatically Controlled Loads (TCLs) such as water and space heating/cooling appliances—has not been fully addressed under this game theoretic approach: their comfort constraints and integer control were not considered. This paper presents a method for properly including TCLs in this framework and discusses its application in energy communities. Specifically, we propose a general mathematical formulation for considering users’ comfort in non-cooperative games. We model the integer nature of the TCLs control with binary variables and show that optimal or close to optimal (less than 1%) solutions are reached. Moreover, different total cost functions can be used depending on the market context and the objective of the demand management program. To illustrate and discuss these aspects in practical applications, we used a case study of an energy community in Spain. The results show that the TC solutions are optimal or only 0.80% worse than optimal; different total cost functions result in different results (load curve smoothing or peak load reduction); consumers’ comfort is respected; and the proposed game model cooperates with consumers in order to minimize community’s costs.

Application of Demand-side Technology in Power System Intelligent Regulation

To reduce peak demand for electricity, smooth load curve shape, improve power system safety and efficiency, this paper, by using intelligent home appliance user operation comfort model is set up to quantify the acceptance, this paper proposes a maximum minimum load management algorithm based on optimization strategy to change electric power use time and power consumption mode.The results show that the proposed model and algorithm can forecast and manage the power load well, and can reduce the peak to average ratio by 14.3% and the total expenditure by 15.3% while maintaining the operating comfort of power users to the maximum.The load management problem of multiple power users can reach Nash equilibrium in a finite number of iterations, and this Nash equilibrium point is also the global optimal point.