A Novel Data-Energy Management Algorithm for Smart Transformers to Optimize the Total Load Demand in Smart Homes
1.1. Literature Review
- It is the first algorithm that proposes a variable soft-constrained power profile limit for each home, in order to limit the power consumption. The limit is based on the classification of homes according to their average energy demand (from the lowest to the highest). This limit will satisfy the system operator by limiting the total power demand on the transformer, hence, reducing the techno-economic losses on the network.
- This is the first algorithm that proposes decentralized demand response and incentive programs for each individual home. In another meaning, at a certain period, home 1 should not exceed 5 kW while, home 2 should not exceed 4 kW. If not, they will not benefit from the incentive programs.
- This is the first algorithm that shares the unused energy between homes, which will maximize the load factor and minimize the power congestion on the network. Hence, the voltage profile is maintained within limits, and techno-economic losses are reduced.
- Our strategy is designed to satisfy both end-users and the system operator, which will be a great advantage to be implemented. That is why it is necessary to define a new incentive program which encourages the end-users to use our strategy to minimize their electricity cost, and to maximize the profit of the system operator under certain constraints.
1.3. Paper Organization
2. The Proposed Data-Energy Management Algorithm for the Smart Transformer
|Step||Description of the Flow Chart in Figure 1|
|1.||ST receives data from the DSO, and the electricity retailer through the WIFI (as presented in Figure 2). Data are stored on a cloud-based platform using the most recent cybersecurity software (e.g., Global System for Mobile Communications (GSM) or Ethernet with the state-of-the-art cybersecurity). Data can be categorized as follows:|
|2.||ST is assigned with a unique identification code in order to access data on the internet|
3. Assumptions for the Study
- The baseload power and water consumption profiles at homes;
- Real weather data such as solar irradiance and ambient temperature;
- Transformer data: 80 kVA, 1 − φ, 60 Hz, 11 kV/120 V;
- Optimized elements at home are similar to reference : 2 EVs, one BSS, one electric water heater, and one Photovoltaic (PV) system.
- Scenario 1: From the perspective of the customers using method 1, which is in favor of the consumers and not the system operator. The main goal of this scenario is to minimize the electricity cost at homes without considering the constraints on the network as in .
- Scenario 2: From the perspective of the system operator using our proposed method 2 without considering our proposed incentive program. This method is called M2woIP (Method 2 without Incentive Program), which is in favor of the system operator and not the customer. In this scenario, the system operator gets the most benefits since it minimizes the techno-economic losses on the network while maximizing its revenue from the electricity bills.
- Scenario 3: In this scenario, we tried to satisfy both end-users and the system operator, in which the end-user pays less than scenario 2 (sometimes even less than scenario 1), and at the same time, the system operator increases its revenue compared to scenario 1, which is due to the minimization of techno-economic losses on the transformer and the network. This scenario is called M2wIP (Method 2 with Incentive Program), in which we implement our proposed incentive program that encourages the users to adopt it and not to be fined in case they do not want to.
4. Results and Discussions
4.1. Impact on Power Consumption at Homes
4.2. Economic Impact on the Electricity Cost at Homes
- M1 in red color;
- M2 without our proposed incentive program (M2woIP) in blue color;
- M2 with our proposed incentive program (M2wIP) in orange color (refer to Table 1).
- 2 $/day is deduced from the electricity bill of the householder if he respects the energy soft-constraint limit at home ();
- 2.9 $/day is deduced if the householder respects the power soft-constraint limit at home ();
- 4.9 $/day is deduced in case the householder is able to respect both energy and power soft-constraint limits;
- 0 $/day is deduced in case the householder could not respect any limits (refer to Table 1).
4.3. Technical Impact on the Distribution Transformer
4.4. Economic Impact on the Distribution Transformer
4.5. Technical Impact on the Network
- OpenDSS is chosen as software to solve the power flow on the distribution network;
- Two particular hours are studied as in Figure 11, 06:00 and 13:00, in which the power demands using M1 attain their maximum and minimum values during a day, respectively;
- IEEE 123-Node Test Feeder is chosen as a distribution network in OpenDSS (Figure 12);
- The distribution network has 123 nodes in which only 85 nodes (black nodes in Figure 12) have loads;
- The black nodes in Figure 12 represent the nodes in which homes with only baseloads are supplied, while the black nodes with red circles represent the nodes in which homes with baseloads and the optimized elements (PV, EWH, BSS, and 2 EVs) are supplied;
- Two different penetration levels (43% and 100%) of nodes with smart homes are considered.
Conflicts of Interest
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|Exceeded Value in %||(Tariff in $/day)||(Tariff in $/day)|
|Consideration||Power Demand||Electricity Cost||Voltage Deviation||LOL of the DT||DT Remaining Lifetime||DT Depreciation Cost||Energy Losses||Electricity Cost of the Energy Losses||Revenue of the DSO|
|Considerations||Unit||Base Load||Method 1||Method 2|
|LOL per Day||day||0.25||7500||1.81|
|DT Remaining Lifetime||day||29,548.46||1.00||4142.96|
|Benchmark Depreciation Cost *||$/day||−0.60||5999.2||0.65|
|Depreciation Cost of the DT||$/day||0.20||6000||1.45|
|Electricity tariff paid by consumers to the DSO||$/Day||5134.9||6442.9||4982.8|
|Cost of total losses on the lines||$/Day||−807.1||−316.4||−316.4|
|Cost of total losses on the transformers||$/Day||−449.5||−260.3||−260.3|
|Depreciation cost of the transformers (Average cost of the transformer is CAD 6000)||$/Day||−294,000||−71.0||−71.0|
|Upgrading cost of the infrastructure|
(≈35% of the actual infrastructure) *
|Total revenue of the DSO||$/Day||−290,121.7||5781.5||4321.4|
|Home||Minimize the electricity cost||3||4 |
|Consumers are satisfied||3||4|
|Low risk of damaging equipment||2||4|
|DT and DN constraints are considered||1||4|
|Reduce the voltage drop||2||4|
|Respect the soft-constraint power limit||1||4|
|Distribution Transformer||Respect the critical power limit||1||4|
|Reduce the Loss of Life||1||4|
|Increase the remaining lifetime||1||4|
|Reduce the depreciation cost||1||4 |
(reduced by 42%)
|Reduce the energy losses and their associated cost||1||4 |
(reduced by 42%)
|Reduce the voltage drop||1||4|
|Reduce the risk of damage and explosion, which may lead to environmental disasters||1||4|
|Reduce installation cost||4||1 |
(higher by 35%)
|Reduce the simulation time *||3||4 |
|Distribution System||Respect the voltage limits (e.g., between 0.95 and 1.05)||2||4|
|Reduce the energy losses and their associated cost||1||4|
(reduced by 80%)
|Increase the total revenue of the DSO||1||4|
|Reduce the investment cost||4||2|
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Share and Cite
El-Bayeh, C.Z.; Eicker, U.; Alzaareer, K.; Brahmi, B.; Zellagui, M. A Novel Data-Energy Management Algorithm for Smart Transformers to Optimize the Total Load Demand in Smart Homes. Energies 2020, 13, 4984. https://doi.org/10.3390/en13184984
El-Bayeh CZ, Eicker U, Alzaareer K, Brahmi B, Zellagui M. A Novel Data-Energy Management Algorithm for Smart Transformers to Optimize the Total Load Demand in Smart Homes. Energies. 2020; 13(18):4984. https://doi.org/10.3390/en13184984Chicago/Turabian Style
El-Bayeh, Claude Ziad, Ursula Eicker, Khaled Alzaareer, Brahim Brahmi, and Mohamed Zellagui. 2020. "A Novel Data-Energy Management Algorithm for Smart Transformers to Optimize the Total Load Demand in Smart Homes" Energies 13, no. 18: 4984. https://doi.org/10.3390/en13184984