Economic Efficiency of the Internet of Things Solution in the Energy Industry: A Very High Voltage Frosting Case Study
Abstract
:1. Introduction
2. Problem Formulation
3. Methodology
3.1. Area 1—Workshops
- Business–this is focused on sales of electrical energy generated in other streams; this is the only stream that directly communicates with the end customer, no matter whether this is in the form of a physical or legal entity [26].
- Facility–according to [27], this is a comprehensive set of support services for the administration of buildings and real-estate property.
- Mining–this is focused on all areas connected with extracting mineral raw materials, such as mining coal and extracting oil, gas, etc. [28].
- Production–this is focused on all areas and methods of electrical energy production, both from renewable sources (the water, wind, and sun) and from fossil sources, etc. [26].
- Distribution–according to [29] its goal is to fully function as an effective administrator for the assets of the distribution system in the power-supply area of electrical energy distribution; in the Czech Republic, it must comply with the conditions of the Energy Act and the rules of the Energy Regulation Office.
3.2. Area 2—IoT Technology Selection
- The Service Level Agreement (SLA) for guaranteeing message delivery.
- The modem’s energy demands and battery life; a long life means a longer service cycle on communicating devices.
- Range in kilometers in environments with various settlement types (rural / urban agglomeration); a higher range means better coverage.
- Downlink capacity; a higher transfer capacity enables the sending of, e.g., images or videos.
- Latency during normal operation.
- Device jammability; easier jamming prevents use in situations where communication needs to be guaranteed.
- Number of end-devices per base station; a fairly high number of devices per base station is a must for high local network capacity, as it enables the use of a large number of sensors in one locality.
3.3. Case Studies and Economic Assessment
4. Results and Discussion
4.1. Selection of Variants for the Case Study
4.2. Business Case: Wind and Glaze-Ice Detection
- The problem being solved: the business case.
- The IoT’s impact on the problem and its proposed solution.
- The effects of applying the IoT to the problem.
- The options for a technical solution.
- The financial calculation.
4.2.1. The Problem Being Solved: The Business Case
4.2.2. The IoT’s Impact on the Problem and Its Proposed Solution
4.2.3. The Effects of Applying the IoT to the Problem
- less frequent energy outages (reduction in case of glaze ice/snow of 75%, windstorm of 15%, etc.)
- reduced costs for servicing trips (the ability to focus strictly on directly threatened localities), especially for wire-repair trips that are never needed because the wire split has been successfully prevented. Total reduction of cost for servicing trips was over 30%.
4.3. The Financial Calculation
4.3.1. Benefits
- prevention of faults caused by glaze ice/snow: savings of 75% of the overall damage
- prevention of faults caused by fallen trees: savings of 20% of the overall damage
- prevention of damage caused by severe windstorms (rows of poles falling): savings of 15% of the overall damage.
- The V1 variant (indirect measurement) are 100%.
- The V2 variant (direct measurement) are 225%.
4.3.2. Costs
- x CZK (100%) for the V1 variant,
- roughly 1.167x (116.7%) for the V2 variant, i.e., a price that is roughly 16.7% higher.
- y CZK as the costs for assembly, development, and testing,
- z CZK as the cost for creating the evaluation system.
- we estimate the V1 variant’s cost in CZK as: 400 * (x + y) + z = 100%,
- we estimate the V2 variant’s cost in CZK as: 400 * (1.167x + y) + z = 110%.
- the sum of the price for IoT communication when the number of devices (400) is multiplied by the yearly per-device price for providing communication (price), i.e. the two prices for IoT Communication = 400 *price_V1 or 400 *price_V2,
- plus, the cost of servicing inspections of IoT devices along with battery replacements, which are to be performed once per 5 years. Since the expected sensor life expectancy is 15 years, this cost is thus 3 * 400 * bp, where bp is the battery price.
- V1 variant: 15 * 400 * price_V1 + 3 * 400 * bp = 100%,
- V2 variant: 15 * 400 * price_V2 + 3 * 400 * bp = 171%.
- The V1 variant (indirect measurement): CZK: 400 * (x + y) + z + 15 * 400 * price + 3 * 400 * bp = 100%,
- The V2 variant (direct measurement): CZK: 400 * (1.167x + y) + z + 15 * 400 * price + 3 * 400 * bp = 120%.
5. Discussion and Conclusions
- Factors from the factory point of view:
- Increasing customer satisfaction in case of less power outage.
- Reducing number of employees needed for managing problematic situations.
- Reducing risks of black-outs.
- Factors from the customer point of view:
- In case of sharing information about temperature – detailed knowledge about temperature.
- Reducing power outage reduces risks related to customer activities.
- Improving quality and stability of services reduces side effects for companies using electricity.
- Factors from the third parties’ point of view:
- Higher profits and smaller costs generate bigger profit, which is related with higher taxes which are payed to the government.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Stream | Priority 1 | Priority 2 | Priority 3 | Priority N/A | Total Identified |
---|---|---|---|---|---|
Business | 4 | 0 | 1 | 30 | 35 |
Facility | 2 | 6 | 9 | 3 | 20 |
Mining, | 1 | 2 | 3 | 4 | 10 |
Production, | 2 | 3 | 6 | 4 | 15 |
Distribution | 7 | 9 | 6 | 22 | 44 |
Total | 16 | 20 | 25 | 63 | 124 |
Characteristic | Unit (Thousands) | Number of Units | ||
---|---|---|---|---|
Region Supplied | km2 | 52 | ||
Number of Consumption Points | Number | 3650 | ||
divided among | large purchasers–VHV and HV | Number | 15 | |
small purchasers–businesses–LV | Number | 436 | ||
small purchasers–residences–LV | Number | 3200 | ||
Maximum Network Load | MW | 6 | ||
Wire length | km | 164 | ||
divided among | VHV | km | 10 | |
HV | km | 51 | ||
LV | km | 104 | ||
Number of Transformer Stations | number | 59 | ||
divided among | Own Stations | Number | 46 | |
Others’ Stations | Number | 13 |
Category | Variant: | V1 | V2 |
---|---|---|---|
Description: | Variant Using a Weather Station with a Glaze-Ice Detection Algorithm and without a Glaze-Ice Sensor | Variant Using a Weather Station with Direct Glaze-Ice Detection (a Sensor) | |
Benefits | One-time Benefits (thous. CZK) | 0 | 0 |
Annual Recurring Benefits (thous. CZK) | 100% | 225% | |
Costs | One-time Costs (thous. CZK) | 100% | 110% |
Annual Recurring Costs (thous. CZK) | 100% | 171% | |
Total Cost of Ownership | Total Cost of Ownership 5 years | 100% | 114% |
Total Cost of Ownership 10 years | 100% | 117% | |
Total Cost of Ownership 15 years | 100% | 120% | |
Overall Balance of Benefits | 5-year Net Profit Value V | Loss | Profit |
Simple Payback Period (in years) | 6 | 3 |
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Maryska, M.; Doucek, P.; Sladek, P.; Nedomova, L. Economic Efficiency of the Internet of Things Solution in the Energy Industry: A Very High Voltage Frosting Case Study. Energies 2019, 12, 585. https://doi.org/10.3390/en12040585
Maryska M, Doucek P, Sladek P, Nedomova L. Economic Efficiency of the Internet of Things Solution in the Energy Industry: A Very High Voltage Frosting Case Study. Energies. 2019; 12(4):585. https://doi.org/10.3390/en12040585
Chicago/Turabian StyleMaryska, Milos, Petr Doucek, Pavel Sladek, and Lea Nedomova. 2019. "Economic Efficiency of the Internet of Things Solution in the Energy Industry: A Very High Voltage Frosting Case Study" Energies 12, no. 4: 585. https://doi.org/10.3390/en12040585