Smart Distributed Energy Storage Controller (smartDESC)

• Published in: Energy (Oxford) 2020-11, Vol.210, p.118500, Article 118500
• Main Authors: Malandra, F., Kizilkale, A.C., Sirois, F., Sansò, B., Anjos, M.F., Bernier, M., Gendreau, M., Malhamé, R.P.
• Format: Article
• Language: English
• Subjects: Advanced metering infrastructure; Control theory; Decentralized control; Distributed control; Distributed generation; Electric water heater; Electrical loads; Energy storage; Game theory; Mathematical analysis; Mean-field; Optimization; Renewable energy; Smart grid; Stochasticity; Thermal analysis
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2020.118500

While the storage properties and the anticipation potential of many classes of power system loads (such as thermal loads) can be exploited to mitigate renewable sources variability, the challenge to do so in an optimal and coherent manner is significant. This is due to the sheer number and dynamic diversity of the loads that can be involved in any large-scale application. The smartDESC concept is a control architecture that was developed for this purpose. It builds on the more pervasive communication means currently available (such as Advanced Metering Infrastructures), as well as the mathematical tools of (i) aggregate load modeling, (ii) renewable energy forecasting, (iii) optimization theory, deterministic or stochastic, and (iv) some recent developments in control of large-scale systems based on game theory, and so-called mean-field (MF) control theory, which allow a scalable yet optimal approach to the decentralized control of large pools of loads. This paper presents the building blocks of the smartDESC architecture, together with an associated simulator and simulation results. •Energy storage potential of Electric Water Heaters is used for load balancing.•This approach permits to increase the penetration of renewable energy sources.•Realistic data in terms of wind forecast and communication infrastructure were used.•Mean-field theory allows a scalable approach to the decentralized control of loads.•Simulation results show benefits for both power operators and consumers.