Title of Proposal: DR-BOB – Demand Response In Block Of Buildings

Call: EE-06-2015

Deadline: June 4, 2015

Budget: ~EUR4 mil. Contact details Dr Vladimir Vukovic | Research Fellow in Engineering, TFI (Technology Futures Institute) Teesside University | Middlesbrough, UK T: +44 (0)1642 34 2406 E: v.vukovic@tees.ac.uk | W tees.ac.uk Introduction Demand Response offers a number of benefits to energy systems including: increased efficiency of asset utilization; supporting increased penetration of renewables on the grid; easing capacity issues on distribution networks to facilitate further uptake of distributed generation on congested local networks; reducing required generator margins and costs of calling on traditional spinning reserve, bringing associated environmental benefits through reduced emissions (SEDC, 2013).

Matching local supply (e.g. solar and wind power) with local demand, including storage, can mitigate congestion on the electricity distribution network enabling savings on investments in grid capacity and congestion management required to support DREG in the electricity industry (Van der Oosterkamp et al., 2014). Supply/demand matching is a key concept underpinning the proposed innovation in the DR-BOB project. The DR-BOB project will demonstrate real-time optimisation of energy demand, storage and supply in blocks of buildings using intelligent energy management systems with the objective of reducing the difference between peak power demand and minimum night time demand (load shifting). This acts to reduce costs and greenhouse gas emissions, and enables better utilization of distributed RES and a reduced need for carbon-intensive peak energy generation. Cost-effective and interoperable solutions will be demonstrated through case studies in the UK, France (?) Spain (?), … Objectives Project will achieve the following objectives:

1. Integrate and augment pre-existing building energy management systems to enable demand-response trade-off at the level of at least 3 buildings within the block.

2. Demonstrate at least 20% energy and 30% cost savings through utilization of buildings as a storage and energy purchase/sell price differentials.

3. Propose and evaluate measures (interfaces, tariffs, business models for different customer segments) to encourage at least 25% of consumers to participate in the envisaged demand response solution.

4. Propose and assess at least 3 levels of technology readiness (1-no capability, 2-some capability, 3-full capability) for consumers’ buildings and local energy infrastructure to participate in the envisaged demand response solution.

The tools and interfaces integrated and demonstrated will include: 1. A scalable energy management system (EMS) to optimise the controllable production, storage/retrieval

and selling of local renewable electricity and heat production in real-time, including generation of accurate energy supply-demand predictions.

2. User interfaces to interact with the occupants of the blocks of buildings including: a. Interfaces required for building energy managers to interact with the services required for Demand

Side Management (DSM), Supply Side Management (SSM) and energy trading. b. Community based interfaces that promote the concept of demand response to building occupants

and encourage behavioural change to support DSM

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The optimisation embedded in the EMS will build on work conducted as part of the IDEAS project.1 The generic approach shown in the Figure 1 shows how the real-word data and predictions will be used for optimisation and decision support. It is planned to handle direct automation with building energy infrastructures via interfaces to building management systems.

Figure 1 Generic Optimization and decision support architecture

Considering a diverse array of resources and building occupants, considerable variation is typically present in energy consumption and production. To effectively operate in variable energy pricing markets, one needs to plan ahead and schedule sales and purchases as accurately as possible at least 24 hours before the time of physical delivery. As such, the concepts of adaptive control (to track and adapt to changing conditions and supply/demand trends) and receding-horizon predictive control (to predict - at regular intervals - the future evolution of the buildings energy balance and re-calculate optimal corrective strategies) will be employed (Camacho and Bordons, 2004). Based upon these concepts, the generic structure of the optimization and decision support system operating upon the ICT infrastructure is presented in Figure 2.

1 IDEAS Collaborative Project (Grant Agreement No. 600071) which co-funded by the European Commission, Information Society and Media Directorate-General, under the Seventh Framework Programme (FP7), Cooperation theme three, “Information and Communication Technologies’, http://www.ideasproject.eu

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Figure 2 Optimization and decision support architecture for an energy manager in blocks of buildings As can be seen in Figure 2, the data flow is periodic; note that it presents a ‘special case’ of the generic concept presented in Figure 1. The operations highlighted in Figure 2 may be split in to five distinct phases: (i) the data acquisition (DAQ) phase, (ii) the prediction (PRE) phase, (iii) the optimization and decision support (OPT) phase and (iv) the post processing (POS) phase and (v) the data distribution (DIS) phase. In the DAQ phase, an ICT platform is employed to acquire measurements of the current state of the blocks of buildings. During the PRE phase, these current and historical state measurements are employed to predict the future energy supply and demand evolution in a block of buildings, along with any other variables of interest such as future spot market prices. During the OPT phase, the optimal corrective strategy to balance supply and demand using the available options for storing, buying or selling energy is determined. During the POS phase, data post-processing, decision support information and KPI calculations are performed. The solution and post-processed data is then distributed or made available during the DIS phase. Note that the optimal solution information may be automatic in nature (the ICT platform is used to automatically send machine to machine commands directly to distributed energy resources), or it may be manual in nature (information is presented to the relevant persons to help support their manual decisions). Technical Concept

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Partners Siemens, UK – main technology provider, coordinator CSTB, FR – monitoring, evaluation, dissemination Teesside University, UK – optimization algorithms, software customisation- can also contribute to business models and exploitation. R2M Solution, Italy – exploitation strategy business models (?) Tata Steel, UK – electro-mechanical storage (flywheel) innovation (?) Iberdrola, ES – public utility, ESCO (?) Northern Power Grid, UK – electrical distribution company Potential demo sites: Teesside University (UK), Montuary (France), Italy??? References Smart Energy Demand Coalition (2013) A Demand Response Action Plan For Europe: http://sedc-coalition.eu/wp-

content/uploads/2013/06/SEDC-DR-FINAL-.pdf Van der Oosterkamp, P. et al. (2014) The role of DSOs in a smart grid environment. Final Report Client: European

Commission: http://ec.europa.eu/energy/sites/ener/files/documents/20140423_dso_smartgrid.pdf Camacho, E.F. and Bordons, C. (2004) Model Predictive Control (2nd Edition) Springer Verlag.