Battery Management Based on Predictive Control and Demand-Side Management: Smart Integration of Renewable Energy Sources

Battery Management Based on Predictive Control and Demand-Side Management: Smart Integration of Renewable Energy Sources

Deepranjan Dongol (University of Applied Sciences Offenburg, Germany), Elmar Bollin (University of Applied Sciences Offenburg, Germany) and Thomas Feldmann (University of Applied Sciences Offenburg, Germany)
Copyright: © 2016 |Pages: 32
DOI: 10.4018/978-1-5225-0072-8.ch007
OnDemand PDF Download:


The chapter is intended to introduce the predictive control based energy management strategy for the grid connected renewable systems in order to achieve an effective demand side management strategy. Grid connected Photovoltaic battery system as being popular and extensively used has been discussed in this chapter .Conventionally, battery storage has been used to store surplus energy produced and meet the load demand with this stored energy. However, such systems do not respond to the grid conditions and violate grid constraints of permissible grid voltage and frequency limits. The operation of the battery depends on the forecast of photovoltaic output and the load demand and as such a predictive control based energy management strategy is needed. A simple optimization problem for such scenarios has also been formulated in great detail to provide readers with an idea for solving such problems. The results of simulations are also discussed.
Chapter Preview

Introduction To Demand Side Management

Demand side management (DSM) is a concept to improve the energy consumption behavior of the consumers by organizing the proper energy consumption pattern. More often DSM is directly associated with reducing the electricity bill for the consumers. For example, the load demand of a household could be reduced or switched off during peak load demand hours when the electricity price is high. Daily activities such as the use of electrical appliances, for example, heating oven, washing machine, etc. which has high power consumption could be operated during hours when the electricity price is low. The dynamic electricity pricing could be seen as an encouragement for the consumers to use off peak load hours to run high power consumption devices and reduce the cost of operation for the gird operators. As the trend towards renewable energy is increasing, even the consumers are switching to renewable energy systems, such as grid connected photovoltaic battery system. As more and more households are becoming equipped with grid connected PV installations with favourable feed-in tariff, it also poses challenges to the maintain grid stability with the existing grid infrastructures. Wirth (2015), mentioned in his book on Recent Facts about Photovoltaics in Germany that the PV generated power covered approximately 6.9% of Germany’s total net electricity consumption in 2014. It also states that “on sunny days, PV power can at times cover 35 percent of the momentary electricity demand and on weekends and holidays up to 50 percent”.

With such trend booming, it is evident that an efficient energy management scheme is required for power flow exchange with the grid. Over a broader scale, besides the best cost of operation for the consumers, the grid capacity and constraints also need to be considered. The grid stability is monitored in terms of permissible voltage and frequency defined by the grid codes. The households are connected to the low voltage grids which are only designed to distribute power form the grid to eh consumers. On days with high solar insolation the grid feeding can be very high and lead to increase in voltage beyond the specified limits at the coupling point. It must be ensured that during the power feed, this increase in voltage is within the permissible limit of the nominal voltage which otherwise could damage the electrical equipment. Likewise, during day time, the PV energy production by far exceeds the load demand and this mismatch between the energy demand-supply needs to be balanced in order to keep the frequency within the permissible limits. The ease of grid integration is important especially when there are large numbers of consumers with grid connected PV system. The surplus PV energy is normally curtailed under such situations. Battery storage provides a mean to store the surplus energy and limit grid feeding. This stored energy could then be used to meet the peak load demand. In other words, the grid power consumption of the consumer is flattened. It is estimated that by 2030 a total of 82 GW rooftop PV installations could be expected and about 52 GW by 2020 in Germany (Jägemann, Hagspiel, &Lindenberger, 2012). The same study also suggested that, the optimized use of PV storage system could on average cover 72% of annual load demand for a single household by self-produced electricity. This would have a significant impact on the energy market with a very high self-reliance and proper DSM from the consumer side.

There have been extensive studies on grid connected renewable systems for DSM over the recent years. Owing to the growth and promotion of renewable energy, several energy management strategies has been proposed over years with different scenarios. The system essentially consists of energy source, load demand and energy storage units. As renewable energy sources are intermittent and vary according to weather, the storage units store surplus energy when the energy production is higher than the load demand and feed the load demand when energy production is weak or none.

This section should provide review to some of the energy management and control strategies proposed for demand side management of renewable energy based systems with storage units. The system design ranges from modelling to different control and optimization strategies based on providing cost effective power flow or maintain the grid stability by ensuring grid voltage within permissible limits or match the energy supply and load demand. Similarly, the optimization and control strategy has been proposed for island systems to provide remote users with best cost of operation in order to reduce the fuel costs.

Complete Chapter List

Search this Book: