Resilient Micro Energy Grids with Gas-Power and ... - IEEE Xplore

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The 2nd IEEE Conference on Power Engineering and Renewable Energy ICPERE 2014

Resilient Micro Energy Grids with Gas-Power and Renewable Technologies Hossam A.Gabbar* (1) Faculty of Energy Systems and Nuclear Science, (2) Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, Oshawa, Canada E-mail: [email protected]

Lowell Bower, Devarsh Pandya, Apurva Agarwal and M.U. Tomal (2) Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, Oshawa, Canada

Abstract— The world is moving towards smart energy grid with green and clean infrastructure which will enable efficient bidirectional energy supply with reduced carbon footprint. Due to increasing energy demands and the pressing issues of efficient energy use, there is a real need to increase the penetration of gas technologies in the power grid. The government of Canada and stakeholders are looking for ways to increase the reliability and sustainability of the power grid; and gas-power technologies may provide a solution. This paper explores the integration of gas and renewable energy generation technologies within various electricity generation scenarios with the goal of developing designs for a resilient micro energy grid (MEG). The distinct scenarios are then evaluated using an advanced algorithm to provide optimum scenario depending on various key performance indicators (KPIs). KPIs to be examined include: economic, power quality, reliability, and environmental friendliness. This work is done using three different systems; geographic information system (GIS) for recording transmission /distribution lines and generation data, a database to store the information, and a MATLAB-based algorithm for evaluating scenarios. These systems are synthesized and represented into a graphical user interface (GUI), where the user defines the zone, area and cell for desired output and system parameters to generate distinct scenarios to identify the optimum generation.

F. R. Islam (1) Faculty of Energy Systems and Nuclear Science, University of Ontario Institute of Technology, Oshawa, Canada

On the other hand, natural gas based power generation is a more reliable and continuous source of energy and is an attractive option for electric power system planners, energy policy makers, operators, regulators, and developers. The optimal uses of energy from the natural gas are still a concern for the researcher [9] and gas power based MEG can provide key solutions for integrating renewable and gas energy resources [10], [11], [12].

Keywords; gas-power systems; micro energy grids; resilient micro energy grids; GIS; KPIs

I.

INTRODUCTION

In recent years smart power grids with renewable energy sources have received increased public attention due to the need, to protect a network from large blackouts, reduce power losses, integrate renewable and sustainable energy generation on a small scale and improve power quality [1], [2]. The sporadic nature of wind speed and solar irradiation make these energy sources intermittent and leading to imbalances [3], [4], [5]. Solar radiations are available during the day, whereas the peak energy demand occurs in the evening and as a result, excess solar energy generation is referred at the time when the exciting grid does not need it [6]. A similar case has been found for wind power generation by various researchers [7], [8].

978-1-4799-6402-4/14/$31.00 ©2014 IEEE

Figure 1: Gas-power based MEG design structure for Ontario

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This paper introduces a model framework for the optimal use of gas-power and renewable energy production to design a reliable and stable MEG system. The structure has been developed considering the Ontario energy model where business process, gas supply, gas to power and power to gas assessment are combined with factors such as the environment, grid topology and defined KPIs. The GIS software has been used to connect components of the gas and power grid to static and dynamic parameters within the database. The algorithm then accesses this data as input for scenario generation, evaluation, and optimization. The total system has been designed while considering requirements of the end user, utility companies, stockholder, service provider and consumer as shown in Fig. 1. The remainder of the paper is organized as follows: Section II provides an overview of the MEG design; Section III presents the gas power grid modeling; resilient MEG evaluation is provided in Section IV; MEG KPIs modeling are presented in Section V; and Section VI contains the results & discussions. Finally, the paper is concluded by brief remarks and suggestions for future work in Section VII.

II.

This section presents the design architecture description for a micro energy grid based on gas power technologies. The detailed use circumstances were examined with the help of the smart grid architecture model defined by IEEE Std P1547 [13]. Figure 3, provides a structural overview of the micro energy grid. The proposed MEG has two main branches, AC voltage bus and DC voltage bus. Micro gas turbine, wind generation, internal combustion (IC) engine based generation, and hydro power are consider to be in the AC voltage bus and battery storage, whereas solar generation and fuel cell are consider to be in DC voltage bus. An inverter connects the AC and DC busses. Loads are divided into four different categories; AC load, DC load, thermal load and gas load. Conversions from electricity to gas and vice versa are also included within the MEG. Hybrid electrical vehicles are considered as a load for both the gas and power systems.

MICRO ENERGY GRIDS (MEG) DESIGN FRAMEWORK

Micro energy grids are fundamentally a substructure of the power grid with low-voltage or medium-voltage distribution systems and distributed energy sources. These types of systems can operate in grid-connected or islanding mode [14]. In this work total Ontario energy grid is considered as regional zone which is a combination of few sub regional zone and the sub regional zones are the summation of cells. The micro energy grid has been considered inside the cell as shown in Fig. 2. KPIs haven designed to develop reliable and stable micro energy grid within the cell which will in turn ensure the reliability of total region.

Figure 3: Micro energy grid framework

III. GAS-POWER GRID MODELING This section identifies functions associated with the modeling and algorithm design. Based on this organization the functional structure of the system control center is given, with the key functional blocks as well as all interfaces. It is the global approach of the system to identify needed functionality and implement it within four different modeling systems; gas supply grid modeling, power grid modeling, hydrogen supply grid and GIS preparation for regional energy supply. Figure 2: Ontario Energy Grid with cell

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Dynamic parameters would include real-time data such as hourly output. Although not yet implemented, consideration has been given to future input of real-time gas and power data analysis with the addition of dynamic parameter categories within the database. Real-time data analysis will assist with accurate mapping of KPIs within gas-power grids and therefore the gas-power efficiency model. An integrated approach to gas-power supply planning is achieved by optimizing potential energy scenarios and integration with risk assessment models. The system is represented in Figure 4. The various loads and generation nodes have been considered and are outlined in Figure 5. IV. SYSTEM ARCHITECTURE The main aim of this study is to optimize Ontario’s gas and power grids to provide a better quality, more reliable, and environmentally friendly power system while satisfying regional energy demands and reducing generation cost. Intergraph’s GeoMedia software is used to assist in the visualization and analysis of the project GIS data.

Figure 4: Gas-power grid modeling framework Major components of the gas and power grid are included in the simulation including; electrical distribution stations, as well as natural gas distribution lines and stations. Component parameters are divided into static and dynamic categories. As an example, static categories within the electrical generation feature class include generation subtype, supply capacity, operator, and IESO code.

Figure 5: GIS mapping for gas-power network of Ontario Gas/electrical lines and nodes are recorded as feature classes with set parameters after their coordinates are confirmed. These classes are associated with tables in the Microsoft Access database. Dynamic parameters for feature classes (e.g. power output of electrical generation sites) are updated using algorithm and presented in the GeoMedia Workspace (see Fig.5). Queries can be performed within the Workspace to filter spatial and non-graphical attributes. The GIS software has been used to divide Ontario into different zones and elements in order to identify the specific location for optimization. Definitions for components of the system are shown in Table 1. Figure 5: Gas-power network load and generation

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Table 1: System definitions

•

Quality

Definition

–

Total Harmonic Distortion for Voltage (THDV)

Largest spatial region in system; based on 10 IESO zones Second largest spatial region in system Smallest spatial region in system Regional zone outside of Ontario (e.g. Manitoba, New York) Line connecting regional and extraregional zone. Point representing generation station or load Transmission/distribution line connecting 2 or more nodes Line connecting 2 regional zones

•

Reliability

–

Capacity Factor (CF) - Static

•

Environmentally Friendly

Label 1

Regional Zone

2 3 4 5

Sub-Regional Zone Cell Extra-Regional Zone Interconnection

6

Node

7

Line

8

Interface

–

CO2 emissions - Static

–

Non-Renewability - Static

–

Risk Impact Factor – Static

The MS Access database is an integral component in this study as it contains all the data that is both visually represented on the GIS software as well as the information needed for the forecasting and evaluation algorithm. The data is distributed categorically into tables and they each hold their respective field names with restricted data types and records. The database is not relational as the GeoMedia workspace is able to control the queries and relationships.

V. MEG KPIS MODELING In order to assess the technology, certain Key Performance Indicators are quantified and included in the KPI analysis of the scenario. Evaluation of scenarios is performed by allowing the user to specify a required power generation by gas technologies (i.e. natural gas turbine, natural gas fuel cell, and hydrogen fuel cell). The scenario is then evaluated by calculating the sum of KPIs for each power generation technology within the chosen scenario. For instance, the cost per kWh associated with solar photovoltaic generation ($ CAD/kWH) is calculated and recorded. Power generation percentages for each technology are then varied (within the prescribed limit) and KPI evaluations performed for each variation. The scenario with the best cumulative KPI score will then be considered the better choice. Weightage can also be assigned to the four KPI categories depending on the requirements of the user. The default setting assumes a 25% weightage for each. The system process is shown in Fig. 6 The KPIs considered for: •

Cost

–

Capital Costs - Static

–

Operational & Maintenance Costs (O&M) - Dynamic

–

Generation Costs - Static

Figure 6: Proposed system process

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The demand for each generation is calculated as:

Table 2: Scenario Evaluation

Ontario

System Parameter

Best Scenario

Normal System

Scenario 4 (40% Gas) – Cost decreased from $15,367,600,000 to $13,875,100,000. Q ~constant, R ~constant, E ~constant Scenario 4 (40% Gas) – Cost decreased from $15,396,900,000 to $14,437,700,000. Q ~constant, R ~constant, E ~constant Scenario 4 (40% Gas) – Cost decreased from $17,694,100,000 to $15,964,200,000. Q ~constant, R ~constant, E ~constant Scenario 4 (40% Gas) – Cost decreased from $656,830 to $610,230. Q ~constant, R ~constant, E ~constant Scenario 4 (40% Gas) – Cost decreased from $656,830 to $618,800. Q ~constant, R ~constant, E ~constant Scenario 4 (40% Gas) – Cost Decreased from $749,420 to $683,620 Q ~constant, R ~constant, E ~constant

15% Increase in Natural Gas Price 15% Increase in Power Demand Toronto

Normal System

VI. RESULT AND DISCUSSIONS To justify the proposed model, a number of scenarios have been analyzed. These scenarios have been modeled based on the contribution of gas power and the current situation. The steps of generating scenarios are as follows: Scenario Modeling •

Models to study:

–

Provincial-level: Ontario

15% Increase in Gas Price

15% Increase in Power Demand

(11.1% power generation by gas technologies) –

Zonal-level: Toronto (13.1% power generation by gas technologies)

•

Scenarios for given models

–

Scenario 1: Current scenario

–

Scenario 2: 20% generation by gas

–


–


Best Cost KPI resulted from 40% generation via gas technology (cost decreased from $749,420 to $683,620, Quality, reliability, and environmental KPIs remain constant as shown in Fig. 7 6. The normal scenario and 15% increasing in gas and power demand with increasing price is given in Table 2.

.Figure 7: KPI evaluation for potential scenario

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VII. CONCLUSION This work guides the user in making an informed decision about the energy mix for a micro energy grid in Ontario through the Publisher Portal tool and GUI that were developed. The scenarios and KPI weights are user inputs and they can be run under dynamic conditions of increase in gas prices or power demands for the future. The historical power demand data can be used in the demand prediction algorithm to find the future demand that needs to be mitigated. Once the evaluation is complete, the resulting generation outputs from specific gas and non-gas technologies are shown in the output window. Results for the MEG and increased power demand are shown in the sample output above. Scenarios were also run for the cell and Toronto zone under normal conditions and increased natural gas cost as well as for Ontario under the 3 system parameters. On the Ontario level under a 25% weightage for each KPI, it is observed that the increase in gas technologies shows savings from $1.73 - $0.96 million dollars with quality, reliability, and environmental friendliness KPIs approximately constant. On the Toronto level, also with even KPI weightage, an increase in gas technologies corresponded with reduced cost while maintaining the quality, reliability, and environmental friendliness. This trend is observed for all 3 system parameters. The system can be enhanced to consider more KPIs such as: line losses and its reliability, the number of fluctuations in frequency, voltage, and current and their durations. Considerations can be made of revenue lost and recorded incidents.

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ACKNOWLEDGMENT Special thanks to industrial partners from Hydrogenics, Veridian, MaRS, Intergraph Canada, and Ontario Power Authority, and to members of the research team at ESCL: Energy Safety and Control Lab at UOIT, Canada. REFERENCES [1]

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Abdelazeem A. Abdelsalam, Hossam A. Gabbar, Adel M. Sharaf, Performance enhancement of hybrid AC/DC microgrid based DFACTS, International Journal of Electrical Power & Energy Systems, Volume 63, December 2014, Pages 382-393 Hossam A. Gabbar, Razibul Islam, Manir U. Isham, Vatsal Trivedi, Risk-based performance analysis of microgrid topology with distributed energy generation, International Journal of Electrical Power & Energy Systems, Volume 43, Issue 1, December 2012, Pages 1363-1375 J. M. Latorre A. Ramos, L. Olmos and I. J. Perez-Arriaga. “Modeling medium term hydroelectric system operation with large-scale penetration of intermittent generation”. In XIV Latin and Iberian Conf. Operations Research, 2008 Prakash K. Ray, Soumya R. Mohanty, and Nand Kishor. “Disturbance detection in grid-connected distributed generation system using wavelet and s-transform”. Electric Power Systems Research, 81(3):805 – 819, 2011. F. Islam, H. Pota, N. Roy, Impact of dynamic phevs load on renewable sources based distribution system, in: IECON 2011-37th Annual

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