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Smart Energy Source Management in a Commercial Building Microgrid

A. C. Vishnu Dharssini*, S. Charles Raja and P. Venkatesh

Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Tamil Nadu, Madurai, India

Abstract

In the most recent times, renewable resource-based local power generation and effective utilization are evolving as a hot research topic, as it mainly focuses on ensuring a reliable energy supply to the consumers. It mitigates the challenges of the volatile nature of renewable sources and uncertainties in demand rise. To ensure the sustainable operation of microgrids specifically, a deep knowledge of demand and generation trends is required. The proposed research framework highlights the promising supply-side management approach of available renewable sources along with the existing grid connection, the optimal energy configuration of which is found with the help of Hybrid Optimization of the Multiple Energy Resources simulation software. The scope of the chapter is to assure the continual supply to meet the load of a particular department block in an educational institution, Thiagarajar College of Engineering (TCE), Madurai (9.8821° N, 78.0816° E). The feasibility study deals with prioritizing available sources such as solar photovoltaic generation, diesel generation, and backup facilities in TCE. Based on seasonal energy trends and availability, a significant demand-side management technique (DSM) technique is feasible for educational buildings: load shifting is implemented. The approach tracks the optimal energy configuration among four configurations and compares the configuration before and after the DSM incorporation. It also pinned out the probability of cost reduction by the optimal source allocation strategy.

Keywords: Building energy management, feasibility analysis, demand side management, optimal source allocation, ToU tariff

1.1 Introduction

In this modern era, the local power generation and utilization concept is considered an asset and efficacious approach to have a better energy management system [1]. This encourages the penetration of renewable energy resources and considerably reduces the usage of conventional fossil fuels [2]. The real-time challenge with the approach is adopting it to an existing grid-connected system without collapsing the system and also ensuring optimal source utilization [3, 4]. The alarming usage of conventional resources which are emitting greenhouse gas paves the way to emphasize the need of incorporating an appropriate demand-side management (DSM) strategy [5]. The DSM techniques encompass various kinds of strategies such as load shifting, peak clipping, valley filling, strategic load growth, strategic load conservation, etc. [6]. Some DSM implemented reduce loads in peak hours, resulting in a demand-response approach [7]. The applicability of DSM with respect to available resources in a location provides a clear insinuation on conventional sources [8]. Some strategies are also tariff-based, aimed to reduce additional charges and switch load in reference to the time of use principle [9]. The preliminary requirement for introducing building energy management is possible with smart energy meters which enable to model the demand pattern of that particular building. The process is generally termed modeling demand, involving the procedural gathering of available information as a numerical note and updating knowledge on the state of existing energy systems [10].

Modeling the demand pattern of smart buildings is essential based on economic and environmental impact on energy utilization trends and sustainable use of available resources [11]. Enormous studies and research articles plotted the problem by various approaches and techniques, but it has a limitation as it varies from system to system and also relies upon system considerations [12]. Each work proposes a different and unique supply and demand-side management approach according to the dataset type, i.e., whether univariate or multivariate. Some researchers focused on comparing supply–demand result patterns [13] accompanied by an analysis of the cost involved in bidding for supply from each kind of source in case of the availability of multiple sources [14].

The study presented in [15] put forth a stochastic programming model for staging the performance of a smart microgrid by reducing the operating cost and emissions with non-conventional resources. Plenty of work dealt with this problem using optimization techniques which are found to be generic and primordial. Vishnupriyan and Manoharan [16] adopted the study with a HOMER simulation software for Hybrid Renewable Energy Systems under six different climatic conditions, and the results were optimized to improve the renewable fraction in both before and after the DSM strategies. Similar works are presented in [17, 18] for residential and commercial buildings, promoting optimal energy sources based on computed net present cost (NPC) and cost of energy (COE).

Figure 1.1 Overview of HOMER analysis.

From this extensive survey, it is clear that predictive DSM implementation by data pertaining to the demand load of higher education institutional buildings was not collected and approached to pick out optimum energy configuration with a standard analytical approach. This paves the way to the initiation of strategic DSM, i.e., load shifting and HOMER software solution as in Figure 1.1.

1.2 Motivations of the Study

• To ensure a balancing operation of the microgrid from both the supply end and the customer end.
• To prioritize the utilization of local generation with available renewable resources which, in turn, results in the reduction of transmission losses.
• To promote building energy management.
• To schedule the operation of each load and to trace corresponding source combinations to feed them.
• To estimate the lifetime expenditure, i.e., over 25 years for each source configuration.

1.3 State of the Art of the System

The test system—Department of Electrical and Electronics Engineering in Thiagarajar College of Engineering—is the study location in the Madurai district of Tamil Nadu, as shown in the map (Figure 1.2). The study area has four sources of supply which are solar PV panels with a net capacity of 18 kW from an overall 450-kW system, a 10-kVA diesel generator system, battery storage of 1 kWh lead acid, and grid supply as depicted in Figure 1.3. The test system consists of highly equipped laboratories with Elmeasure smart meters affixed to trace the energy usage pattern. The data gets continually stored in the server which is reclaimed in the centralized server system on demand using Elmeasure’s ElNet software.

1.4 Overview of the Proposed Methodology

Figure 1.4 shows the overall process initiating from data fetching to optimal configuration selection. The data obtained from various smart meters will possess certain default shortcomings that can be offset by adopting proper data handling techniques. There exists a subsequent process to get coherent worthwhile data sources at the end of the data pre-processing approach. These data catered to the analytics part to draw insights from the data. In the proposed methodology, data is fed to the decision tree algorithm which decides on loads to be operated at a particular time. In short, it schedules the primary load and secondary loads in a building with respect to time. Following this, it incorporates the most probable DSM strategy, i.e., load shifting. Next, for effective supply side management, load patterns before and after DSM are laid hold of and fed to the HOMER software. It compares each configuration in terms of environmental, technical, and economic aspects, resulting in a best approach (before/after DSM with appropriate supply-side management).

Figure 1.2 Location of Thiagarajar College of Engineering.

Figure 1.3 Block diagram of the test system.

Figure 1.4 Process flow of the proposed methodology.

1.5 DSM Approach

The loads and appliances in the test system are tagged as primary and secondary loads based on customer preferences and utilization premises. The essential loads for the active functioning of laboratories apart from test kits and devices are tagged as primary loads, while the rest, for sophistication in utilization, are tagged as secondary loads. The categorization of loads in the building is presented in Table 1.1.

Lighting loads, fans, routers, projectors, and plug points are considered primary, while air conditioners, water dispensers, exhausters, printers, and coolants are secondary loads. With the knowledge of available loads and their importance, loads are preferred accordingly. Tracing the existing pattern of consumption is essential as it is one of the determining factors for implementing DSM.

The most probable and effective DSM approach—load shifting—is applicable as it involves the effective usage of loads without violating any kind of limitations. It prevents reaching the maximum demand limit and thereby ensures the neglect of penalties. The previous consumption record which is properly maintained in the server is retrieved and treated to feed the machine learning algorithm. The decision tree algorithm thus extracts...