Energy scheduling of a smart microgrid with shared photovoltaic panels and storage: The case of the Ballen marina in Samsø

This paper focuses on the Model Predictive Control (MPC) based energy scheduling of a smart microgrid equipped with non-controllable (i.e., with fixed power profile) and controllable (i.e., with flexible and programmable operation) electrical appliances, as well as photovoltaic (PV) panels, and a ba...

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Bibliographic Details
Published in:Energy (Oxford) Vol. 198; p. 117188
Main Authors: Carli, Raffaele, Dotoli, Mariagrazia, Jantzen, Jan, Kristensen, Michael, Ben Othman, Sarah
Format: Journal Article
Language:English
Published: Oxford Elsevier Ltd 01.05.2020
Elsevier BV
Elsevier
Subjects:
algorithms
batteries
boats
Computer Science
data collection
Demand side management
Denmark
Distributed generation
energy costs
Energy management
Energy storage
energy transfer
heat pumps
Information engineering
linear programming
Microgrid
Model predictive control
Modeling and Simulation
On-line scheduling
Optimization algorithm
Photovoltaics
planning
prices
Renewable energy
solar collectors
solar energy
solar farms
time series analysis
wastewater
Italy
Denmark
Model predictive control
Demand side management
Optimization algorithm
Microgrid
Renewable energy
On-line scheduling
Energy management
Energy storage
model predictive control
renewable energy
on-line scheduling
energy storage
microgrid
energy management
optimization algorithm
demand side management
ISSN:0360-5442, 1873-6785
Online Access:Get full text
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Summary:This paper focuses on the Model Predictive Control (MPC) based energy scheduling of a smart microgrid equipped with non-controllable (i.e., with fixed power profile) and controllable (i.e., with flexible and programmable operation) electrical appliances, as well as photovoltaic (PV) panels, and a battery energy storage system (BESS). The proposed control strategy aims at a simultaneous optimal planning of the controllable loads, the shared resources (i.e., the storage system charge/discharge and renewable energy usage), and the energy exchange with the grid. The control scheme relies on an iterative finite horizon on-line optimization, implementing a mixed integer linear programming energy scheduling algorithm to maximize the self-supply with solar energy and/or minimize the daily cost of energy bought from the grid under time-varying energy pricing. At each time step, the resulting optimization problem is solved providing the optimal operations of controllable loads, the optimal amount of energy to be bought/sold from/to the grid, and the optimal charging/discharging profile for the BESS. The proposed energy scheduling approach is applied to the demand side management control of the marina of Ballen, Samsø (Denmark), where a smart microgrid is currently being implemented as a demonstrator in the Horizon2020 European research project SMILE. Simulations considering the marina electric consumption (340 boat sockets, a service building equipped with a sauna and a wastewater pumping station, and the harbour master’s office equipped with a heat pump), PV production (60kWp), and the BESS (237 kWh capacity) based on a public real dataset are carried out on a one year time series with a 1 h resolution. Simulations indicate that the proposed approach allows 90% exploitation of the production of the PV plant. Furthermore, results are compared to a naïve control approach. The MPC based energy scheduling improves the self-supply by 1.6% compared to the naïve control. Optimization of the business economy using the MPC approach, instead, yields to 8.2% savings in the yearly energy cost with respect to the naïve approach. •A new framework for the smart energy management of a marina is proposed.•Controllable loads, shared resources, and demand-supply balance are considered.•A model predictive control approach for the optimal energy scheduling is detailed.•Energy cost and self reliance are analyzed extensively using a public real dataset.•The proposed approach effectively improves profit and sustainability.
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ISSN:0360-5442
1873-6785
DOI:10.1016/j.energy.2020.117188