Energy scheduling of a smart microgrid with shared photovoltaic panels and storage: The case of the Ballen marina in Samsø
Introduction
Due to increasing concerns on environment sustainability, policy makers, researchers, and practitioners have recently devoted attention to strategic actions aimed at energy efficiency and energy savings [[1], [2], [3]]. The growing energy demand and penetration of renewable energies pose several challenges to grid operators and thus motivate the acclaimed roll-out of the smart grid concept [4]. This concept refers to the enhancement of the traditional power grid infrastructure, especially in terms of pervasive use of information and communications technologies [[5], [6], [7]]. Indeed, to reduce the generation of energy from fossil fuels and expand the use of renewable energy sources, new advanced solutions for the future power grids need to be explored [8,9]. Smart grids are also attractive from a management and control perspective, since decision and control actions aimed at optimally controlling power flows between components should be considered in order to achieve the economic and environmental profitability of their operations [10]. In particular, the motivation for moving from the traditional energy systems paradigm of “generation-follows-load” to the new paradigm of “load-follows-generation” has led to developing Demand Side Management (DSM) methodologies [11]. These methodologies play an important role for the sustainable development of both district heating [12] and electrical distribution networks [13]. The term DSM is generally used to indicate a set of actions aimed at efficiently controlling and managing the energy consumption of a site, mainly to decrease the costs incurred for the supply of electricity, including network and system charges, and reduce the need for investments in networks and/or power plants for meeting peak demands [14]. These optimization actions are aimed at modifying the characteristics of the energy consumption, in terms of the overall entity of consumption, time profile of consumption, and grid connection parameters, in order to bring the energy demand and supply closer to a perceived optimum [14].
In basic implementations of DSM, an in-depth analysis of energy consumption of a site (e.g., building, network of buildings, facility, district, etc.) allows determining its own peculiarities of each site and understanding if the consumption habits can be optimized without the use of additional tools [15]. Alternatively, the on-site installation of renewable sources (e.g., photo-voltaic panels, wind turbines), cogeneration plants as well as energy storage systems (e.g., electrochemical, thermal storages) -also known as distributed generation and storage-enables collection of energy from many sources and leads to lower environmental impacts while improving security of supply [16]. However, such a variety of small, grid-connected devices implies the need of an Energy Management System (EMS), which allows to monitor the dynamics of all the involved assets (loads, batteries, generators), optimize in real-time the performance of storage and generation systems, and leverage on deployed assets to provide the power grid with supplementary services [17]. Indeed, all these components must autonomously and efficiently cooperate for the optimal demand response of end-users and efficient interaction with power grid, smart appliances, renewable energy sources (RESs), battery energy storage systems (BESSs) and plug-in electric vehicles (PEVs) [14]. As a consequence, the necessity of developing an effective optimal energy scheduling framework tackling all these objectives is apparent.
Coping with this challenge, 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 BESS that allows storing surplus of energy for later use. 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 [54]. 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 real public dataset [59] 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.
The remainder of this paper is organized as follows. Section II provides an overview of the literature on microgrid energy scheduling and positions the paper contribution. Section III describes the detailed model of the system under study. The naïve and MPC-based formulations of the scheduling problem are presented in Section IV and Section V, respectively. The case study results and analysis are provided in Section VI to demonstrate the benefits of the proposed strategy. Finally, the paper ends with conclusions and future work in Section VII.
Section snippets
Literature review
There is a large literature on energy management of microgrids. In the context of DSM, the aim of these systems is essentially to enable users to respond to market mechanisms and determine the optimal schedule of the energy activities. The literature reports several methods for energy scheduling. In general, two types of strategies can be found as described in detail in the sequel: offline scheduling [[18], [19], [20]] and real-time scheduling [[22], [23], [24], [25], [26], [27], [28], [29],
System model
In this section we present a mathematical model related to the microgrid of a marina, which is aimed at formulating the energy scheduling problem for the electrical appliances and the shared resources, i.e., the RES and the BESS as well as the demand-supply balance and constraints.
We consider the marina as a small port area including facilities such as docks equipped with electrical sockets for boats, a service building used by sailors, and an office building for the harbour master. The
Naïve energy scheduling strategy
For the sake of defining a baseline strategy, in this section we describe the simplest possible approach for determining time slot-by-time slot the amount of energy to be consumed by each CL, the amount of energy to be charged/discharged into/from the BESS, and the amount of bought/sold from/to the grid.
The proposed naïve control strategy is formally expressed by Algorithm 1 (Fig. 2). This algorithm is composed by two stages: the naïve scheduling of CLs and the scheduling of the BESS and energy
MPC energy scheduling strategy
Inputs: , , Procedure: 1 set 2 set 3 iterate 4 set beginning of the nearest sauna period of use 5 set end of the nearest sauna period of use 6 if 7 set 8 elseif 9 if 10 set 11 end 12 else 13 set 14 end 15 16 set 17 end Outputs:
Case study
In this section we show the performance of the proposed MPC algorithm through numerical experiments. Specifically, we consider a period of analysis of one year (i.e., ) with a prediction horizon of 24 h (i.e., ) and a sampling time of 1 h. The obtained results are reported in the sequel and are analyzed and compared with the previously defined naïve control strategy. In particular, five different cases are analyzed (Table 1). In the first case the naïve control
Conclusions
Smart energy systems must be developed in close correlation and interplay with advanced control strategies to meet the challenge of integrated fluctuating renewable energy sources (RES) and achieve a balance between generation and consumption. Microgrids with energy storage capacity and load flexibility play a crucial role in this process. Coping with this, we propose a Model Predictive Control (MPC) based energy scheduling for the Demand Side Management (DSM) of a microgrid equipped with
Acknowledgement
This paper presents work that is partially supported by the Horizon 2020 research programme through the project SMILE (Smart Island Energy systems) under grant agreement no. 731249. The article is based on an oral presentation delivered at the 5th conference on Smart Energy Systems (SES) in Copenhagen, Denmark, September 10–11, 2019.
References (60)
- et al.
Sustainable and cost-efficient energy supply and utilisation through innovative concepts and technologies at regional, urban and single-user scales
Energy
(2019) Composite index for benchmarking local energy systems of Mediterranean port cities
Energy
(2015)- et al.
4th Generation District Heating (4GDH). Integrating smart thermal grids into future sustainable energy systems
Energy
(April 2014) - et al.
Smart energy and smart energy systems
Energy
(October 2017) - et al.
Demand side management in district heating networks: a real application
Energy
(2019) - et al.
A method for technical assessment of power-to-heat use cases to couple local district heating and electrical distribution grids
Energy
(2019) - et al.
Demand side management in smart grid: a review and proposals for future direction
Sustainable Cities and Society
(2014) - et al.
Smart operations of smart grids integrated with distributed generation: a review
Renew Sustain Energy Rev
(2018) - et al.
Smart Energy Systems for coherent 100% renewable energy and transport solutions
Appl Energy
(May 2015) - et al.
Multi-objective optimization for decision-making of energy and comfort management in building automation and control
Sustainable Cities and Society
(2012)
Optimal power management for nanogrids based on technical information of electric appliances
Energy Build
Control strategies for decreasing energy costs and increasing self-consumption in nearly zero-energy buildings
Sustainable cities and society
Predictive control techniques for energy and indoor environmental quality management in buildings
Build Environ
Generalised control-oriented modelling framework for multi-energy systems
Appl Energy
A model predictive control framework for reliable microgrid energy management
Int J Electr Power Energy Syst
Enterprise-wide optimization for industrial demand side management: fundamentals, advances, and perspectives
Chem Eng Res Des
Demand side management of a run-of-mine ore milling circuit
Contr Eng Pract
A model predictive control strategy for load shifting in a water pumping scheme with maximum demand charges
Appl Energy
Model predictive control: theory and practice—a survey
Automatica
A survey on residential demand side management architecture, approaches, optimization models and methods
Renew Sustain Energy Rev
Business and socioeconomic assessment of introducing heat pumps with heat storage in small-scale district heating systems
Renew Energy
Sociotechnical transition to smart energy: the case of Samso 1997-2030
Energy
Evaluation of electricity storage versus thermal storage as part of two different energy planning approaches for the islands Samsø and Orkney
Energy
Energy recovery from the organic fraction of municipal solid waste: a real options-based facility assessment
Sustainability
Smart grid — the new and improved power grid: a survey
IEEE Communications Surveys Tutorials
A high precision phase control unit for DDS-based PLLs for 2.4-GHz ISM band applications
IEEE 58th International Midwest Symposium on Circuits and Systems (MWSCAS)
Incremental adaptive semi-supervised fuzzy clustering for data stream classification
IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)
The status of 4th generation district heating: research and results
Energy
Demand side management: demand response, intelligent energy systems, and smart loads
IEEE Transactions on Industrial Informatics
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