Experimental validation of a real time energy management system for microgrids in islanded mode using a local day-ahead electricity market and MINLP
Introduction
Modern energy management and control systems could help to reduce the cost of energy. However, they are applied either in a complex manner or a too simple way to achieve the desired goal. To maximize energy savings, minimize related costs and obtain a fast payback in MG systems, it is vital and most desirable to optimally operate an aggregated number of micro-sources to incur the lowest possible production cost. In MG, this can be achieved by applying optimization methods and adjusting the generators output to minimize the production costs. The optimization procedure may interact with public network information. For example, energy for storage devices can be bought when prices are low, and sold when required.
Various proposals for EMSs with different optimization methods and different MG structures have already been presented in literature. [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18]. Some references have developed optimization methods for EMSs aiming to obtain scheduling operations and optimal operating strategy [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11]. The objective function (OF) in these references allows autonomous or grid connected decision-making to determine the hourly optimal dispatch of generators depending on system constraints and market parameters. The economic concepts of EMSs in the MG market and the development of strategies to achieve such benefits are reported in [12], [13], [14], [15], [16], [17], [18].
Another investigated concept in this paper deals with demand response (DR). It is used by electric utilities to manage customer electricity consumption in response to supply conditions. Utilities encourage customers to reduce their consumption at critical times periods or in response to market prices. Currently, generation and transmission system facilities are oversized to cover peak demand plus a margin for forecasting error and unforeseen events. Smoothing such peak demand could lead to cost and size reduction of the plant. Some systems, such as DR, may encourage energy storage to arbitrage within periods of low and high demand (or low and high prices). In the literature, there are several studies investigating DR concept in MGs for different applications, for instance: demand shifting and peak shaving [19], [20], [21], [22], [23], DR exchange in which DR is treated as a public good to be exchanged between buyers and sellers [24], load and generation profiles control [25], [26], incentive based DR regulation considering penalties for customers in case of no load reduction response [27] and the combination of distributed interruptible load shedding and dispatched micro-sources to manage the network by distribution system operators [28]. The study reported in this paper is based on the previous work by the authors, where the design of a modified conventional EMS based on LEM (MCEMS−LEM) for a MG system is reported and experimentally tested on IREC’s MG; and no optimization was used in [1]. In this paper, a real-time EMS–MINLP−LEM is proposed for MG system and a comparison between the MCEMS−LEM and the proposed algorithm demonstrates that the proposed algorithm achieves better results.
Section snippets
Problem formulation
The system under analysis encompasses a stand-alone Wind Turbine (WT) – Photovoltaic (PV) – Microturbine (MT) – Energy Storage (ES) system. The mathematical model of the proposed EMSs is introduced in the following subsections. Details of the mathematical implementation of the MCEMSLEM can be found in [1]. For that reason, it is not repeated in this paper.
The proposed algorithms
In this study two algorithms, named MCEMS−LEM and EMS–MINLP−LEM, are considered. These algorithms are presented in the following subsections.
Application to MG Testbed
Simulation and experimental evaluations are performed for a stand-alone WT–PV–MT–ES system shown in Fig. 4. The IREC Testbed is presented in Fig. 5. In short, the Testbed is built from power emulators able to generate or consume any desired power profile. Detailed explanation concerning the structure and configuration setting is found in [1], [34], [35]. As, it is observed from Fig. 4, this system has a central controller to which data will be sent. This data includes the offer of each
Results and discussions
To show the performance of the proposed algorithms, a case study is presented. The studied MG comprises renewable energy sources (WT and PV), and a power and heat combining unit (MT in this study). These are connected to an energy storage system (a battery in this study), as shown in Fig. 4. The outputs of the algorithms are explained in the following four subsections including execution time of the algorithms, the outputs of the EMS unit, the outputs of DR unit and finally the outputs related
Conclusions
This paper has compared two algorithms to implement an EMS based on LEM in MGs. The proposed LEM has been introduced to allow the owner of the generation units to establish their own strategy for participating in MG generation with minimum information shared between micro-sources. Indeed, fulfilling the customer’s requirements with minimum COE is the main theme of this paper. Likewise, DR has been used to avoid the penalty cost due to UP as well as to improve demand side management.
Acknowledgments
This research was supported by EIT and KIC-InnoEnergy under the projects KIC-ASS (30-2011-IP26-KIC ASS) and EVCITY (28-2011-IP24-EVCity) and by the European Regional Development Funds (ERDF, “FEDER Programa Competitivitat de Catalunya 2007–2013”).
The authors thank Jonathan Fournier and Manel Sanmarti for his valuable points of views and language editing.
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