Активный потребитель: задача оптимизации потребления электроэнергии и собственной генерации
As a part of managing behavior of an active consumer of electric power in prospective smart grids it is necessary to create a mathematical model that meets his or her economic interests. Existing models either do not take into account all relevant aspects or turn out to be too complicated for the purposes of multi-agent modeling. We suggest a mathematical model of an active consumer and use it to investigate the problem of consumption and local generation regimes optimization. We derive conditions when the consumer’s problem has a pretty simple and efficient solution. The proposed approach is illustrated by optimizing the operating modes of equipment for a single household.
Development of Russian electric power industry in recent years is characterized by a multitude of problems and a decrease in a number of performance indicators. It dissatisfies consumers and encourages them to implement various measures to reduce risks and costs of energy supply. This creates preconditions for the emergence of «active» consumers in the domestic electric power industry. Given this trend it would be appropriate to switch from Supply Side Management to Demand Side Management. This will require the implementation of a wide range of measures, including strategic issues of industry development, legal framework and transition to a customer-centric market model.
To solve problems of demand management in terms of smart energy systems (Smart grid), we need a mathematical model of active consumer decision-making. Existing models either do not consider important aspects of consumer behavior, or are too complex for use in multi-agent simulation. A mathematical model of an active consumer is proposed, based on which we formulate and solve the problem of optimization of electrical appliances and consumer equipment, as well as determine the loading conditions of self-generation, under which the consumer problem allows simple and effective solution. The proposed approach is illustrated by equipment optimization of a single household.
n the context of electricity demand response, an important task is to generate accurate forecasts of energy loads for groups of households as well as individual consumers. We consider the problem of short-term (one-day-ahead) forecasting of the electricity consumption load of a residential building. In order to generate such forecasts, historical energy consumption data are used, presented in the form of a time series with a fixed time step. In this paper, we first review existing (one-day-ahead) forecasting methodologies including: a) naive persistence models, b) autoregressive-based models (e.g., AR and SARIMA), c) triple exponential smoothing (Holt-Winters) model, and d) combinations of naive persistence and auto-regressive-based models (PAR). We then introduce a novel forecasting methodology, namely seasonal persistence-based regressive model (SPR) that optimally selects between lower- and higher-frequency persistence and temporal dependencies that are specific to the residential electricity load profiles. Given that the proposed forecasting method equivalently translates into a regression optimization problem, recursive-least-squares is utilized to train the model in a computationally efficient manner. Finally, we demonstrate through simulations the forecasting accuracy of this method in comparison with the standard forecasting techniques (a)-(d)
The need for accurate balancing in electricity markets and a larger integration of renewable sources ofelectricity require accurate forecasts of electricity loads in residential buildings. In this paper, we considerthe problem of short-term (one-day ahead) forecasting of the electricity-load consumption in residentialbuildings. In order to generate such forecasts, historical electricity consumption data are used, presentedin the form of a time series with a fixed time step. Initially, we review standard forecasting methodologiesincluding naive persistence models, auto-regressive-based models (e.g., AR and SARIMA), and the tripleexponential smoothing Holt-Winters (HW) model. We then introduce three forecasting models, namelyi) the Persistence-based Auto-regressive (PAR) model, ii) the Seasonal Persistence-based Regressive (SPR)model, and iii) the Seasonal Persistence-based Neural Network (SPNN) model. Given that the accuracy ofa forecasting model may vary during the year, and the fact that models may differ with respect to theirtraining times, we also investigate different variations of ensemble models (i.e., mixtures of the previ-ously considered models) and adaptive model switching strategies. Finally, we demonstrate through sim-ulations the forecasting accuracy of all considered forecasting models validated on real-world datagenerated from four residential buildings. Through an extensive series of evaluation tests, it is shown thatthe proposed SPR forecasting model can attain approximately a 7% forecast error reduction over standardtechniques (e.g., SARIMA and HW). Furthermore, when models have not been sufficiently trained, ensem-ble models based on a weighted average forecaster can provide approximately a further 4% forecast errorreduction.