Monetary Value of a District’s Flexibility on the Spot- and Reserve Electricity Markets

In the future, advanced multi-energy systems are expected to handle an increasing share of fluctuating renewable energy generation through the management of multiple advanced energy conversion and storage technologies operating across different energy carriers. The market diffusion of such concepts of Local Energy Management—the management of energy supply, demand, and storage within a given geographical area—is expected to provoke a fundamental reorganization of the power generation sector. This work contributes to this topic by estimating the maximum potential economic value attained from using the flexibility of a district to take advantage of operating within multiple electricity markets at the same time. The study is based on the measured demand and production data of a newly built suburban residential district located in Central Switzerland. The actual configuration of the district and the resulting flexibility, as well as an extension with a battery storage system, is used to estimate the economic value of the flexibility. Then, an optimization algorithm manages flexible demand, production, and storage capacities in order to alternatively maximize the revenues/cost savings, self-sufficiency, or share of renewable resources of the district’s energy supply. In this vein, the impact of the way the system operates in the markets regarding the degradation of the battery is assessed and its pay-back-time is estimated. The analysis revealed a considerable profit potential associated with the district thermal and electricity storage flexibility, in particular, when operating on both the spot and reserve electricity markets. Firstly, it was shown that overall energy costs can be minimized through an optimal management of energy conversion and storage systems. Secondly, complementing the infrastructure with batteries and trading flexibility on the spot market would decrease costs by about 43%, while an additional 20% cost decrease could be captured by including trading on the reserve market. Thirdly, it has been shown that operation on the spot- and reserve market does not seem to degrade the battery more than solely operation on the spot market. However, when operating on the spot- and reserve markets, battery amortization would still take about 10 years.

[1]  Viktor Dorer,et al.  Optimisation of a district energy system with a low temperature network , 2017 .

[2]  Asgeir Tomasgard,et al.  Multi market bidding strategies for demand side flexibility aggregators in electricity markets , 2018 .

[3]  Pierluigi Mancarella,et al.  Techno-economic and environmental modelling and optimization of flexible distributed multi-generation options , 2014 .

[4]  J. G. Slootweg,et al.  Demand response for real-time congestion management incorporating dynamic thermal overloading cost , 2017 .

[5]  Ming Jin,et al.  MOD-DR: Microgrid optimal dispatch with demand response , 2017 .

[6]  Ivana Kockar,et al.  The economics of distributed energy generation: a literature review , 2015 .

[7]  C. Jivacate,et al.  Particle swarm optimization for AC-coupling stand alone hybrid power systems , 2011 .

[8]  Goran Andersson,et al.  The Energy Hub: A Powerful Concept for Future Energy Systems , 2007 .

[9]  Jan Carmeliet,et al.  Towards an energy sustainable community: An energy system analysis for a village in Switzerland , 2014 .

[10]  Rudi A. Hakvoort,et al.  Assessing the costs of electric flexibility from distributed energy resources: A case from the Netherlands , 2019, Sustainable Energy Technologies and Assessments.

[11]  Asgeir Tomasgard,et al.  Prosumer bidding and scheduling in electricity markets , 2016 .

[12]  Paulien M. Herder,et al.  Local Alternative for Energy Supply: Performance Assessment of Integrated Community Energy Systems , 2016 .

[13]  Rodolfo Dufo-López,et al.  A novel lifetime prediction method for lithium-ion batteries in the case of stand-alone renewable energy systems , 2018, International Journal of Electrical Power & Energy Systems.

[14]  R. Hakvoort,et al.  Managing electric flexibility from Distributed Energy Resources: A review of incentives for market design , 2016 .

[15]  Pierluigi Mancarella,et al.  Integrated energy and ancillary services provision in multi-energy systems , 2013, 2013 IREP Symposium Bulk Power System Dynamics and Control - IX Optimization, Security and Control of the Emerging Power Grid.

[16]  Pierluigi Mancarella,et al.  Multi-energy systems : An overview of concepts and evaluation models , 2015 .

[17]  Kyri Baker,et al.  Modeling stationary lithium-ion batteries for optimization and predictive control , 2017, 2017 IEEE Power and Energy Conference at Illinois (PECI).

[18]  François Maréchal,et al.  Distributed model predictive control of energy systems in microgrids , 2015, 2016 Annual IEEE Systems Conference (SysCon).

[19]  Pierluigi Mancarella,et al.  Distributed multi-generation: A comprehensive view , 2009 .

[20]  Kristina Orehounig,et al.  Integration of decentralized energy systems in neighbourhoods using the energy hub approach , 2015 .

[21]  J. Moriarty,et al.  Risk-sensitive optimal switching and applications to district energy systems , 2014, 2014 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS).