Demand-side management in smart grid operation considering electric vehicles load shifting and vehicle-to-grid support
Introduction
The transition towards the Smart Grid (SG) requires to incorporate new functionalities and capabilities to the existing electricity grid. Among some identifiable features, distributed generation is a common characteristic of the SG and, in addition, the nature of these generators is varied since they can be non-dispatchable renewable, such as wind turbines or photovoltaic panels, combined heat and power, fuel cells, microturbines or diesel-powered plants. Devices which are able to store energy, like electric fixed batteries, can help the system to smooth the intermittent behavior of renewable sources enabling an easier integration. The next generation of the electricity grid will also pave the way to electrified transportation [1]. SGs comprise different entities that can interact with each other bidirectionally, giving the possibility to establish commercial relationships to serve and request electric energy or to solve technical problems that could arise, thus empowering the consumer. These entities within the SG can respond to changes in the prices at which the energy is bought and sold to the main grid with the objective of minimizing the costs of the energy they need or maximizing the income from the energy they sell. Among the many features that make a grid smart, the essential aspect is the integration of power system engineering with information and communication technologies. In turn, this integration can allow for advances in reliability, efficiency and operational capability [2].
Among other interesting characteristics of SGs, the concept of Demand-Side Management (DSM) has attracted the attention of many researchers and, among DSM strategies, demand response has been widely considered [3], [4], [5]. Demand response can be understood as voluntary changes by end-consumers of their usual consumption patterns in response to price signals [6]. Along with the savings regarding electricity bills, this kind of schemes can be used to avoid undesirable peaks in the demand curve that take place in some time periods along the day, resulting in a more beneficial rearrangement [7], [8], [9], [10]. Through the use of DSM, several benefits are expected, like the improvement in the efficiency of the system, the security of supply, the reduction in the flexibility requirements for generators or the mitigation of environmental damage, although some challenges have to be overcome starting from the lack of the necessary infrastructure [11]. In addition, the introduction of DSM has to be conceived taking into account other distributed energy resources technologies that could be present in SGs [12], [13]. In regard to this, several SG projects worldwide are underway or have been completed [14], [15].
In order to simplify the implementation of the proposed approach in real systems, most of the actions in the SG are taken by the agents. In order to take these actions, the agents act on their own interest; sometimes, however, they make use of additional information provided by the SG operator. The individual decisions of the agents can only be slightly corrected by the SG operator (centralized correction) in order to correct the violation of technical constraints in the SG, in case they arise.
To make decisions each agent poses an optimization problem to maximize its profit over a set of periods, they can perform DSM strategies and Vehicle-to-Grid (V2G). As energy prices are usually higher for high demand periods, the optimization problems result in a flattened load curve. Following the regulatory trends in many countries, in particular in countries in Europe [16], the renewable generation in the model is also prioritized over the conventional generation. The framework considers market and technical operation of the grid, and it is illustrated in a case study modeling a SG based on IEEE 37-bus distribution grid.
The rest of the paper is organized as follows: in Section 2, the related work and paper contributions are introduced. Then, Section 3 presents a proposal for the SG operation, referred as problem statement, including the description of the operational algorithm steps previously mentioned. Section 4 focuses on DSM strategies, describing the optimization problems in detail and providing models of the different elements included. The case study and results are presented in Section 5. Finally, conclusions are drawn in Section 6.
Section snippets
Related work and contributions
Many works are found in the literature investigating DSM benefits and SG modeling. Some of them cover problems regarding scheduling appliances based on pricing models; for instance, in [17], a voluntary household load shedding model is studied in order to keep the system in secure conditions with respect to demand peaks. Using two different methodologies, the benefits of DSM for both the consumers and utilities are shown in [18], [19], stressing the importance of identifying the flexible loads.
Problem statement
The problem is posed on a SG that includes distributed generation (renewable and non-renewable), and EVs that can perform V2G operations. We study the operation in a typical day on a hourly basis, under the assumption that a similar operation could be done for each day. The problem is multi-period and thought to be applied for planning purposes, that is, not in real time.
The agents in the considered SG can be of two types: (a) EVs, these agents can move among the nodes in the network, also one
Description of the agent strategy
In this section, the strategy followed by the agents to make their decisions regarding load shifting is described. In what follows, first the different SG elements of the agents and the main related parameters and variables are introduced. Then, the agent optimization problem, consisting of an objective function and a set of constraints, is presented.
Case study and results
To illustrate how the proposed method works, it is applied to a case study for the SG depicted in Fig. 5. Different operating conditions characterized by the number of periods that loads can be shifted, parameter “k”, are studied. The SG consist of 8 agents, with 2 renewable generators, 5 non-renewable generators, 2 batteries and 14 EVs on a network based on the IEEE-37 bus distribution grid [41].
In what follows, first the data for the case study are described and second the main results
Conclusion
A SG model relying on DSM strategies has been proposed. In this model, agents are modeled through optimization problems, with the possibility of flattening of the daily electricity load curve, shifting the demand from one time period to other time periods in response to hourly prices. It has been shown that it can be applied to common grid loads and EVs charging, helping to allocate the demand more efficiently.
The particular characteristics of the load curve, the requirements for EVs mobility,
Acknowledgment
The authors would like to acknowledge the financial support from the Ministerio de Economía y Competitividad through Project ENE-2011-27495 and from the Junta de Andalucía through Proyecto de Excelencia with Ref. 2011-TIC7070.
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