Die Stadt als Speicher Making Cities Smarter

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1 Die Stadt als Speicher Making Cities Smarter Workshop Grid + Storage, Munich, Stefan Kippelt, TU Dortmund University 3/31/2016

2 Motivation increasing demand for flexibility in the power system The Energiewende creates several fields of tension in the power system for its market players Increasing divergency between: Rural areas with decentral generation from RES Urban region with an increasing number of flexible units Increasing number of flexible loads and gen. units new business models for municipal utility comp. decreasing costs for communication increasing relevance of the distribution grid Project concept: Provision of flexibility by making urban areas smarter easier market access

3 BESS Project Goals Design of an Virtual Energy Storage by pooling of various flexibility options Practical assessment of the storage potential under real-life conditions Evaluation of different coordination and communication strategies Development of new business models for municipal utility companies Development of mechanism for the avoidance of grid congestions (and grid expansions) EV Heat Pumps PV CHP ~ ~ ~ Ancillary Services Markets Grid

4 The Consortium distributed optimization, grid issues and project management field test, customer care system control and interfaces central optimization platform and forecasts business models for aggregators communication architecture

5 Units and Objectives Regarded Flexibility Options: heat pumps (2) micro CHP (1) (district heating) CHP (4) night storage heating (2) battery storage system (1) solar battery storage (1) Objectives spot und intraday markets compensation of local RES feed-in peak load reduction of distribution grid provision of control reserves electric vehicles

6 Management Concept Markets and Objectives objectives global constraints distr. grid ~ ~ ~ Virtual Storage central management central and/or distributed optimization device device device device + + generation loads

7 Central Management Approach central approach Solution of central dispatch problem by means of mathematical programming (MILP, MIQP) Backtesting using a (stochastic) robust optimization approach Pros: high adaptability to new objectives and restrictions good observability of system behavior Cons: limited scalability central aggregation of potentially sensitive data

8 Distributed Management Approach Schedule Manager Distributed optimization approach based on randomized Lagrange relaxation Iterative solution of the optimization problem by exchange of (virtual) price signals and aggregated schedules Pros: very high scalability fail-safe architecture local local local local sensitive data stay at local s Cons: limited observability (bypassed by data backdoor for demo) high bandwidth and data volume required DC: data concentrator complex implementation of global constraints

9 Consideration of Local Grid Restrictions Non-discriminating consideration of existing or impending grid restrictions Modelling and analysis of grid topology Forecast of grid load, disregarding flexible units Determination of available grid capacity for the utilization of the virtual storage Compilation of constraints for the optimal dispatch of the virtual storage Aggregator 1 broadcast Aggregator N

10 Day-Aheadschedule ICT Infrastructure schedule manager DC Metering and data logging Distributed Optimization DC DC local local interface connector (PLC) pilot unit ext. forecast providers power forecasts Central Server load flow calc. optimization market interactions Metering and data logging visualization Metering and data logging Central Optimization local interface connector (PLC) pilot unit *) PLC = programmable logic controller

11 Project Status today MS 1 MS field test conceptual design hardware implementation setup MS 3 MS 4 field test evaluation study of tech. potentials design of sim. tools business models potentials of involved units market-compliant definition potentials of involved regions interdependencies with distribution grids design of market products (B2B, B2C) future potentials development and regulation validation and backtesting validation

12 Stakeholders Reduction of supply costs utility companies Customer Retentions through innovation and advanced products Optimized grid utilization Competitive advantages against discount suppliers Further participation in the Energiewende private customers ICT Development of new ICT strategies Opening new markets Making accessible new providers of flexibility Reduction of grid investments power system OEM Added Value for existing products

13 PHELIX Day-Ahead 2012 Exemplary Results CHP Unit el. Power: 5.5 kw el thermal power: 12.5 kw th Storage capacity: 2,000 L resp. 45 kwh th Supplied building: Office building power in kw el price in /MWh power in kw el average daily generation profile optimized heat lead provided flexibility ~5 kwh time of day

14 Lessons Learned Customer care is very important important Supplemental intervention in the unit s operation can be very complex and costly units should be smart in the first place be prepared for surprises Marketing potentials primarily depend on regulatory framework and pricing components Fixed price components overlay incentives for flexibility Currently no opportune accounting processes

15 Project Status today MS 1 MS field test conceptual design hardware implementation setup MS 3 MS 4 field test evaluation study of tech. potentials design of sim. tools business models potentials of involved units market-compliant definition potentials of involved regions interdependencies with distribution grids design of market products (B2B, B2C) future potentials development and regulation validation and backtesting validation

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