Adaptive Management of Energy Packets

The Electric Power Grid is an excellent example of a Cyber-Physical Systems (CPS) which includes sources of fossile and renewable energy, together with electro-mechanical conversion systems, electric energy transport systems, physical systems which consume energy, and distributed computer systems and networks that serve to monitor and control the system as a whole. With the increasing introduction of ICT into the Power Grid, we are seeing that all the classical issues of distributed systems and networks are coming into the heart and core of the issues related to energy. When renewable energy is used energy storage also becomes very useful. Storage units can include electric car batteries and uninterruptible power supplies, dams, and compressed air units. The presence of intermittent sources of energy, and storage units lead to an analogy between computer networks which store and forward and download data, and electrical networks that furnish energy both from direct production and storage. Thus this paper discusses Energy Packet Networks (EPNs), a concept for systems which store and forward quantised energy units from generators and renewable sources, to storage and consumers. An EPN can also be a framework for energy harvesting networks at the micro-scale, and bio-nano scale energy networks. We present a mathematical model for EPNs and analyse an example the where stored energy and a fast ramp-up source are used to meet random energy surges assuming a storage unit and a random flow of renewable energy. We compute the probability that all requests are satisfied, or that some cannot be satisfied, and the spill over rate of renewable energy, and then evaluate the effect of an imperfect communication and computer-control system where message losses and delays can degrade the energy system performance.

[1]  James W. Taylor.  Density forecasting for the efficient balancing of the generation and consumption of electricity , 2006 .

[2]  Erol Gelenbe,et al.  Probabilistic models of computer systems , 1976, SIGMETRICS '76.

[3]  Erol Gelenbe,et al.  Energy harvesting communication networks: Optimization and demonstration (the E-CROPS project) , 2013, 2013 24th Tyrrhenian International Workshop on Digital Communications - Green ICT (TIWDC).

[4]  R. Ramanathan,et al.  Short-run forecasts of electricity loads and peaks , 1997 .

[5]  Ufuk Topcu,et al.  A simple optimal power flow model with energy storage , 2010, 49th IEEE Conference on Decision and Control (CDC).

[6]  Erol Gelenbe,et al.  Global behavior of homogeneous random neural systems , 1991 .

[7]  Phil Taylor,et al.  Evaluating the benefits of an electrical energy storage system in a future smart grid , 2010 .

[8]  S.S. Venkata,et al.  Wind energy explained: Theory, Design, and application [Book Review] , 2003, IEEE Power and Energy Magazine.

[9]  Erol Gelenbe,et al.  Energy packet networks: smart electricity storage to meet surges in demand , 2012, SimuTools.

[10]  Ismael Sánchez,et al.  Adaptive combination of forecasts with application to wind energy , 2008 .

[11]  G. Winkler,et al.  Intelligent energy management of electrical power systems with distributed feeding on the basis of forecasts of demand and generation , 2001 .

[12]  Erol Gelenbe,et al.  A probability model of uncertainty in data bases , 1986, 1986 IEEE Second International Conference on Data Engineering.

[13]  José Ramón Cancelo,et al.  Forecasting the electricity load from one day to one week ahead for the Spanish system operator , 2008 .

[14]  Erol Gelenbe,et al.  Analysis of Update Synchronization for Multiple Copy Data Bases , 1979, IEEE Transactions on Computers.

[15]  G. Sinden.  Characteristics of the UK wind resource: Long-term patterns and relationship to electricity demand , 2007 .

[16]  Erol Gelenbe,et al.  Energy Packet Networks: ICT Based Energy Allocation and Storage - (Invited Paper) , 2011, GreeNets.

[17]  Erol Gelenbe,et al.  Steps toward self-aware networks , 2009, CACM.

[18]  S. Koopman,et al.  An Hourly Periodic State Space Model for Modelling French National Electricity Load , 2007 .

[19]  Erol Gelenbe,et al.  G-Networks with resets , 2002 .

[20]  Erol Gelenbe,et al.  G-networks with multiple classes of signals and positive customers , 1998, Eur. J. Oper. Res..

[21]  J. Zack.  Overview of Wind Energy Generation Forecasting New York State Independent System Operator Truewind Solutions, Llc , 2004 .

[22]  D.G. Infield,et al.  Potential for Domestic Dynamic Demand-Side Management in the UK , 2007, 2007 IEEE Power Engineering Society General Meeting.