Managing Energy in a Virtual Power Plant Using Learning Classifier

Managing Energy in a Virtual Power Plant Using Learning Classifier Systems Oliver Kramer, Benjamin Satzger, Jörg Lässig International Computer Science Institute, Berkeley CA 94704, USA {okramer, satzger, jla}@icsi.berkeley.edu http://www.icsi.berkeley.edu/ Abstract—Many renewable energy resources like wind- Section II we summarize related work in the field of virtual turbines or solar collectors are volatile and unstable. Distributed power plant models and optimization. Section III concentrates energy producing entities are clustered to virtual power plants on a description of the virtual parameterizable energy con- that – from a black-box perspective – should act like conventional stable power plants. It is the task of intelligent control strategies sumers and producers. We will learn controller strategies for to compensate fluctuations and to exploit renewable recourses to energy storage systems and compensating power producers, a maximal extend. In this paper we present a simple simulation as well as pricing for simple demand side management with a model of a virtual power plant that is based on real-world power learning classifier system (LCS). The LCS will be described consumption and wind production data. A power storage system in Section IV, while the experimental results will be presented allows to save overcapacities and to supply energy in case of demand bursts. A reserve power plant allows additional energy in Section V. The last Section VI summarizes the results and production to compensate shortcomings. The simulated power provides an outlook to future research directions. plant can easily be extended by parameterizable modules. We use a learning classifier system to evolve rules that control the II. RELATED WORK energy storage system and the reserve plant to compensate energy Distributed energy supply by renewable resources becomes fluctuations. A study of evolutionary parameters as well as a discussion of the evolved rules complement the experimental ana- more and more important. Willis and Scott [10] describe lysis. A simple demand side management example demonstrates planning and evaluation of distributed power systems in detail. the influence of pricing on the virtual power plant. Davis [6] compares existing central station generation and distributed resources with regard to various aspects like lia- I. INTRODUCTION bility, power quality, infrastructure requirements or electrical Renewable electric energy systems like wind mills, biomass environmental effects. Based on this discussion he shows or solar energy have an important part to play in modern the significant advantages of distributed power systems such energy grids. Renewable electric energy production moves as micro-grids. The idea to cluster distributed generators to today’s energy grids from a centralized single supply system virtual power plants is discussed by Stothert et al. [16] or by towards a decentralized bidirectional grid of suppliers and Dondi et al. [7]. consumers. But renewable resources are often volatile and Some examples show that evolutionary algorithms (EAs) temporally unsteady. Their determining factors cannot be con- and other CI techniques can be successfully applied to prob- trolled, and are difficult to predict. To compensate instabilities lems in the field of renewable energy and smart grids. Mosetti and fluctuations in energy production, modules are clustered et al. [15] optimized the wind turbine distribution of a wind to virtual power plants. The movement towards renewable farm in order to extract maximum energy for minimal in- plants with higher degrees of efficiency, e.g., based on fuel stallation cost. The optimization process is based on a wind cells, and towards large energy storage systems, increase the farm simulation model subdividing an area into 100 square chances of successful virtual power plants that may be able to cells of possible turbine locations. Caldon et al. [5] have replace conventional producers. Energy management systems introduced a virtual power plant simulation model taking into of the future will have the task to predict power demand and account electric and heat production. They use a stochastic production of fluctuating producers like wind turbines or solar optimization algorithm and concentrate on objectives like collectors, and to develop control strategies to balance power minimization of short term variable production. Wang and consumption and production under uncertainty. Singh [2] address the problem to integrate different power In this paper we describe a virtual power plant simulator sources like wind turbines, solar panels and storage batteries that models power consumption based on real-world data on with regard to the objectives cost, reliability and pollutant the demand side, and various types of power producers based emissions. To solve this multi-objective optimization problem, on parameterized entities. The simulation model can be used they introduce a multi-objective particle swarm optimization to learn energy system management strategies that compensate algorithm. Recently, Kramer et al. [13] introduced a memetic fluctuations, and to analyze the interplay between various types approach of evolutionary optimization and a local variant of of controllers. Methods from computational intelligence (CI) kernel regression to forecast power consumption in smart turn out to be well appropriate to solve these problems. In grids. III. THE VIRTUAL POWER PLANT MODEL we model noise that may be added to the original data as follows: In the following, we describe the virtual power plant model. It is based on various entities modeling typical components, ec(t) = −eEG(t) · (1 + γ1 ·N (0; 1)): (1) e.g., renewable power resources, an energy storage system, and an additional energy production entity that is able to The noise strength can be controlled by parameter γ1. compensate consumption bursts or a lack of renewable energy. Figure 1 illustrates the participating modules of the virtual MW 4000 plant. Consumers have to be supplied with energy. For this sake various energy resources are available. Wind mills play 3500 the role of the renewable volatile energy sources in our virtual plant. A storage system1 stores overcapacities and is able to 3000 deliver stored energy with regard to an energy loss factor. 2500 A reserve plant can produce additional energy, and has the capacity to cover the whole energy demand. The total power 2000 of the system taking into account all consumers and producers P at time t is denoted by η(t) = i ei(t), and is the sum of all 1500 producers with ep(t) ≥ 0 and consumers with ec(t) ≤ 0. Energy Consumption, Feb 7 Energy Consumption, Feb 28 1000 Wind Production, Feb 7 Wind Productoin, Feb 28 500 0 10 20 30 40 50 60 70 80 90 100 t Fig. 2. Examples for energy consumption and wind data from the EIRGRID data source for the two Sundays Feb 7, and Feb 28 that will be subject to the experimental analysis in Section V. To allow interesting interactions between storage systems and reserve plant, the wind production is scaled into the range of the power consumption, see Section V-A. Wind farm Smart Consumers control B. Wind Energy In our current virtual power plant model, wind is the renewable ”fluctuating” resource we plan to concentrate on. The data for the produced wind energy is also taken from the Reserve plant EIRGRID data resources, i.e., from an EIRGRID wind farm. Energy Storage Figure 2 shows the wind production on Feb 7, and Feb 28, scaled by factors ' = 5:0, and ' = 15:0 to reach levels Fig. 1. Illustration of the virtual power plant model consisting of consumers that allow interesting interactions between renewable energy that get their energy from a wind farm, a reserve plant, and an energy storage production and consumption. We assume that module w 2 P system. in the set of energy producers P generates a certain amount of energy ew(t) at time t. Again, we allow to add noise with strength γ2 to the original data: A. Consumer Model ew(t) = eWIND(t) · (1 + γ2 ·N (0; 1)): (2) Consumers are modeled as follows. We assume that each Wind energy is volatile and its prediction is no easy underta- c 2 C C entity in the set of energy consumers consumes a king. Wind power prediction can help to improve turbine con- e (t) t certain amount of energy c at time . To reflect a realistic trol and the integration of wind power into the grid [8]. Various consumption scenario, the basis of the energy consumption approaches have been introduced to predict the production of e (t) is real energy data EG measured and published by the wind energy based on weather forecasts [14], [12]. According IR RID 96 Irish energy company E G [1]. Each day consists of to Focken et al. [19] the prediction of energy produced by values measured in mega-watt (MW), i.e., one value every wind has sufficiently been solved. A recent discussion about 15 minutes. Figure 2 shows the power consumption on two 2 the integration of wind energy has been published by Grant Sundays in February 2010 . To simulate fluctuations that et al. [9]. Forecasting is a rather important aspect. To model may occur depending on consumer behavior and number of uncertainty in wind prediction, we recommend two variants. modules on the demand side, and to avoid an overadaptation, First, the future time steps can be subject to an increased magnitude of noise γ^ . Second, besides the actual energy that 1or battery 2 2We have chosen February 7, and February 28, because of characteristic has been produced, EIRGRID has published an estimation of wind production on these days. produced energy that can alternatively be used for prediction, while the actual production can be used to determine the total In the scenario of compensating fluctuating energy, it is of energy η(t) in the virtual power plant. interest to supply all consumers, while at the same time minimizing η(t), i.e., to maintain η(t) > for a safety C.

Load more