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Prof. Adela Bara
University of Economic Studies, Department of Economic Informatics and Cybernetics, Bucharest, Romania

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0 Artificial Neural Networks
0 Big Data
0 Business Intelligence
0 Data Mining
0 Data Warehousing

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Big Data
Machine Learning
Artificial Neural Networks
Business Intelligence
IoT
Informatics solutions for energy systems (data integration, analytics, web-services, cloud-computing development)

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Journal article
Published: 28 July 2021 in Computers & Electrical Engineering
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Although fraud in electricity consumption is easier to detect when consumption is recorded hourly by smart meters, in most developing countries, where the propensity for fraud is higher, conventional meters are not yet affordable. Fraud detection is easier with time series data-logging due to the periodicity and variability of consumption that reveals deviations from a regular consumption pattern. In contrast, fraud detection with conventional meters remains a significant challenge because anomalies in consumption are well hidden within the normal consumption of other consumers. In this paper, large datasets regarding consumers and invoice data from Tunisia are combined and investigated with several Machine Learning (ML) classification algorithms, to detect irregularities in electricity consumption. By performing extensive feature engineering, including multivariate Gaussian distribution, the efficiency of ensemble classifiers such as Light Gradient Boosting (LGB) outperforms other algorithms and achieves realistic performance from challenging, unbalanced and uncorrelated input datasets.

ACS Style

Simona-Vasilica Oprea; Adela Bâra. Machine learning classification algorithms and anomaly detection in conventional meters and Tunisian electricity consumption large datasets. Computers & Electrical Engineering 2021, 94, 107329 .

AMA Style

Simona-Vasilica Oprea, Adela Bâra. Machine learning classification algorithms and anomaly detection in conventional meters and Tunisian electricity consumption large datasets. Computers & Electrical Engineering. 2021; 94 ():107329.

Chicago/Turabian Style

Simona-Vasilica Oprea; Adela Bâra. 2021. "Machine learning classification algorithms and anomaly detection in conventional meters and Tunisian electricity consumption large datasets." Computers & Electrical Engineering 94, no. : 107329.

Journal article
Published: 07 July 2021 in Knowledge-Based Systems
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The grid congestion and imbalance problems are nowadays approached through a new perspective of the energy flexibility services that are usually acquired through a bidding process by aggregators on behalf of grid operators and electricity retailers and traded via Local Flexibility Markets (LFM). They can be also acquired at a fixed price under specific conditions of interruption and operation. The flexibility services are offered by electricity consumers and prosumers that own generation and storage facilities and controllable appliances, adding value to the emerging Information and Communications Technology (ICT) and smart metering technologies. In this paper, we propose an adaptive direct load optimization and control with an Internet of Things (IoT) architecture and compare it with a classic approach of Direct Load Control (DLC). The optimization process consists in a day-ahead scheduling that aims to minimize the electricity expense using programmable appliances (shift) and their operational constraints, whereas the adaptive DLC comes on top of it with additional load control (shed) using flexibility to cope with real-time uncertainties including the temperature comfort of the consumers. As the consumption is recorded using smart meters and appliances that continuously generate large volumes of data at different time resolution and formats requiring fast analytical and decisional processes, the challenge consists in correlating and integrating the optimization requirements with IoT and appliance control within an edge and fog computing architecture to overcome grid congestion, power capacity scarcity and forecast errors. The simulations are performed using real open datasets consisting in 114 single-family houses that form a small community with modern and flexible appliances, providing that the electricity bill reduction is up to 22.62%. Furthermore, to validate, several indicators for consumers and aggregator are proposed: total daily used flexibility decreased on average by 21.05%, number of interruptions also decreased on average by 20.51%, maximum number of interruptions per appliance decreased by 58.33%, while Peak to Average Ratio (PAR) improved by 32% when implementing the proposed DLC architecture.

ACS Style

Simona-Vasilica Oprea; Adela Bâra. Edge and fog computing using IoT for direct load optimization and control with flexibility services for citizen energy communities. Knowledge-Based Systems 2021, 228, 107293 .

AMA Style

Simona-Vasilica Oprea, Adela Bâra. Edge and fog computing using IoT for direct load optimization and control with flexibility services for citizen energy communities. Knowledge-Based Systems. 2021; 228 ():107293.

Chicago/Turabian Style

Simona-Vasilica Oprea; Adela Bâra. 2021. "Edge and fog computing using IoT for direct load optimization and control with flexibility services for citizen energy communities." Knowledge-Based Systems 228, no. : 107293.

Journal article
Published: 15 March 2021 in Energy Policy
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The new potential of Distributed Energy Resources (DER), residential consumers, buildings, and prosumers in terms of controllable devices, self-generation, and storage makes them more active in the market encouraged by lower electricity prices. In addition, integrating a higher volume of volatile Renewable Energy Sources (RES) that unstress the public grid by local trading interactions is a desirable target of Local Electricity Markets (LEM). In this paper, we propose a blockchain trading mechanism to simulate the electricity transactions for 11 modern smart houses with more than 300 appliances, 8 roof- or faced-PV systems, and smart-metered 15-min readings that form a small-size community. The electricity generated at the community level lowers the electricity bills and brings benefits for prosumers (sellers) and consumers (buyers). Several trading mechanisms for LEM transactions including auctions such as Uniform Price (UP), Pay-As-Bid (PAB), Generalized Second-Price (GSP), Vickrey-Clark-Groves (VCG) methods are implemented to evaluate the benefits and show their efficiency. After the market is initially cleared, an adjustment coefficient of the price is proposed for both sides (seller and buyer) to enlarge the trading potential at the community level using blockchain technology. It proves to bring excellent results to the LEM participants and enhance trading with outstanding benefits.

ACS Style

Simona-Vasilica Oprea; Adela Bâra. Devising a trading mechanism with a joint price adjustment for local electricity markets using blockchain. Insights for policy makers. Energy Policy 2021, 152, 112237 .

AMA Style

Simona-Vasilica Oprea, Adela Bâra. Devising a trading mechanism with a joint price adjustment for local electricity markets using blockchain. Insights for policy makers. Energy Policy. 2021; 152 ():112237.

Chicago/Turabian Style

Simona-Vasilica Oprea; Adela Bâra. 2021. "Devising a trading mechanism with a joint price adjustment for local electricity markets using blockchain. Insights for policy makers." Energy Policy 152, no. : 112237.

Conference paper
Published: 01 March 2021 in INTED2021 Proceedings
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ACS Style

Anca-Alexandra Ducman; Simona-Vasilica Oprea; Adela Bâra; Gabriela Ene; Cătălin Ceaparu; Vlad Diaconita. ANALYZING THE RESULTS OF SWITCHING FROM TRADITIONAL TO ONLINE CLASSES. INTED2021 Proceedings 2021, 1465 -1470.

AMA Style

Anca-Alexandra Ducman, Simona-Vasilica Oprea, Adela Bâra, Gabriela Ene, Cătălin Ceaparu, Vlad Diaconita. ANALYZING THE RESULTS OF SWITCHING FROM TRADITIONAL TO ONLINE CLASSES. INTED2021 Proceedings. 2021; ():1465-1470.

Chicago/Turabian Style

Anca-Alexandra Ducman; Simona-Vasilica Oprea; Adela Bâra; Gabriela Ene; Cătălin Ceaparu; Vlad Diaconita. 2021. "ANALYZING THE RESULTS OF SWITCHING FROM TRADITIONAL TO ONLINE CLASSES." INTED2021 Proceedings , no. : 1465-1470.

Conference paper
Published: 01 March 2021 in INTED2021 Proceedings
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ACS Style

Anca-Alexandra Ducman; Simona-Vasilica Oprea; Adela Bâra; Cătălin Ceaparu; Gabriela Ene. ANALYZING THE IMPACT OF NONFORMAL EDUCATION IN WINE MARKETING. INTED2021 Proceedings 2021, 1449 -1457.

AMA Style

Anca-Alexandra Ducman, Simona-Vasilica Oprea, Adela Bâra, Cătălin Ceaparu, Gabriela Ene. ANALYZING THE IMPACT OF NONFORMAL EDUCATION IN WINE MARKETING. INTED2021 Proceedings. 2021; ():1449-1457.

Chicago/Turabian Style

Anca-Alexandra Ducman; Simona-Vasilica Oprea; Adela Bâra; Cătălin Ceaparu; Gabriela Ene. 2021. "ANALYZING THE IMPACT OF NONFORMAL EDUCATION IN WINE MARKETING." INTED2021 Proceedings , no. : 1449-1457.

Journal article
Published: 05 February 2021 in Sustainability
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Demand response (DR) programs were usually designed to provide load peak reduction and flatten the load curve, but in the context of rapid adoption of emerging technologies, such as smart metering and sensors, load flexibility will address current trends and challenges (such as grid modernization, demand, and renewables growth) encountered by the evolving power systems. The uncertainty of the renewable energy sources (RES) and electric vehicle (EV) fleet operation has increased the importance of load flexibility that can be managed to provide more support for the stable operation of power systems, including balancing. In this paper, we propose a data model to handle load flexibility and take advantage of its benefits. We also develop a methodology to collect and organize data, combining the consumption profile with several auxiliary datasets such as climate characteristics of the location, independent system operator (ISO) to which the consumer is affiliated, geographical coordinates, assessed flexibility coefficients, tariff rates, weather forecast for day-ahead flexibility forecast, DR-enabling technology costs, and DR programs. These multiple features are stored into a flexibility relational database and NoSQL database for large consumption data collections. Then, we propose a data processing flow to obtain valuable insights from numerous . csv files and an algorithm to assess the load flexibility using large residential and commercial profile datasets from the USA, estimating plausible values of the flexibility provided by two categories of consumers.

ACS Style

Simona-Vasilica Oprea; Adela Bâra; Răzvan Cristian Marales; Margareta-Stela Florescu. Data Model for Residential and Commercial Buildings. Load Flexibility Assessment in Smart Cities. Sustainability 2021, 13, 1736 .

AMA Style

Simona-Vasilica Oprea, Adela Bâra, Răzvan Cristian Marales, Margareta-Stela Florescu. Data Model for Residential and Commercial Buildings. Load Flexibility Assessment in Smart Cities. Sustainability. 2021; 13 (4):1736.

Chicago/Turabian Style

Simona-Vasilica Oprea; Adela Bâra; Răzvan Cristian Marales; Margareta-Stela Florescu. 2021. "Data Model for Residential and Commercial Buildings. Load Flexibility Assessment in Smart Cities." Sustainability 13, no. 4: 1736.

Journal article
Published: 26 November 2020 in IEEE Access
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The progress of ICT technologies, day-ahead forecast, home energy management systems, implementation of smart meters, and Distributed Energy Sources (DER) enables new business opportunities for prosumers to locally trade the surplus via blockchain platforms leading to considerable advantages at the community level. The current research handles settlement similar to a centralized market that it is not necessarily the best solution for blockchain. Nonetheless, the settlement is essential as sellers and buyers perceive the attractiveness of the local trading through the market results. In this paper, we propose two novel and efficient settlement mechanisms (Global Balancing Settlement GBS and Splitting Settlement SS) for Peer-to-Peer (P2P) electricity exchange enhancing the performance of the classic Pairwise Settlement PS. These will be written as stored procedures embedded into the smart contracts along with auctioning procedures. The simulations are performed using a small residential community with 30% of the electricity that can be locally traded to lower the bills and unstress the public grid. The performance of the two proposed settlement methods is proved by the 14 scenarios that thoroughly indicate that GBS and SS provide better results for both sellers and buyers than PS. In the reference scenario, with GBS, sellers have the highest encashments with almost 4% more, whereas buyers encounter the lowest payments with almost 5% less than in case of the classic settlement. Starting from reference scenario, alternative scenarios are envisioned to extend the analyses and assess the performance of the settlement mechanisms. The highest gain is recorded with GBS mechanism: almost 8.8% for sellers and 6.5% for buyers. Another interesting outcome is that GBS is providing better results than SS. When deviations are small, SS provides almost 6% gain for both sellers and buyers, but when they increase, the gain is exceedingly small or none.

ACS Style

Simona-Vasilica Oprea; Adela Bara; Anca Ioana Andreescu. Two Novel Blockchain-Based Market Settlement Mechanisms Embedded Into Smart Contracts for Securely Trading Renewable Energy. IEEE Access 2020, 8, 212548 -212556.

AMA Style

Simona-Vasilica Oprea, Adela Bara, Anca Ioana Andreescu. Two Novel Blockchain-Based Market Settlement Mechanisms Embedded Into Smart Contracts for Securely Trading Renewable Energy. IEEE Access. 2020; 8 (99):212548-212556.

Chicago/Turabian Style

Simona-Vasilica Oprea; Adela Bara; Anca Ioana Andreescu. 2020. "Two Novel Blockchain-Based Market Settlement Mechanisms Embedded Into Smart Contracts for Securely Trading Renewable Energy." IEEE Access 8, no. 99: 212548-212556.

Journal article
Published: 16 November 2020 in Computers & Electrical Engineering
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The consumption data from smart meters and complex questionnaires reveals the electricity consumers’ willingness to adapt their lifestyle to reduce or change their behaviour in electricity usage to flatten the peak in electricity consumption and release the stress in the power grid. Thus, the electricity consumption can support the enforcement of tariff and demand response strategies. Although the plethora of complex, unstructured and heterogeneous data is collected from various devices connected to the Internet, smart meters, plugs, sensors and complex questionnaires, there is an undoubted challenge to handle the data flow that does not provide much information as it remains unprocessed. Therefore, in this paper, we propose an innovative methodology that organizes and extracts valuable information from the increasing volume of data, such as data about the electricity consumption measured and recorded at 30 min intervals, as well as data collected from complex questionnaires.

ACS Style

Simona-Vasilica Oprea; Adela Bâra; Bogdan George Tudorică; Maria Irène Călinoiu; Mihai Alexandru Botezatu. Insights into demand-side management with big data analytics in electricity consumers’ behaviour. Computers & Electrical Engineering 2020, 89, 106902 .

AMA Style

Simona-Vasilica Oprea, Adela Bâra, Bogdan George Tudorică, Maria Irène Călinoiu, Mihai Alexandru Botezatu. Insights into demand-side management with big data analytics in electricity consumers’ behaviour. Computers & Electrical Engineering. 2020; 89 ():106902.

Chicago/Turabian Style

Simona-Vasilica Oprea; Adela Bâra; Bogdan George Tudorică; Maria Irène Călinoiu; Mihai Alexandru Botezatu. 2020. "Insights into demand-side management with big data analytics in electricity consumers’ behaviour." Computers & Electrical Engineering 89, no. : 106902.

Journal article
Published: 21 October 2020 in IEEE Access
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The impressive advantages offered by of demand-side participation have accelerated deployment of demand response (DR) programs. However, the first step to attain the benefits of DR programs is to increase awareness level of the customers. This paper proposes a simple-but-efficient platform to enlighten the costumers on manifested merits of demand-side load control. The proposed platform is a web-based application which acquires the load profile of the customer, associated flexible appliances, and the customer preferences for using the appliances. In turn, presents the optimal operation schedule for flexible appliances and attained benefits from using the optimal schedule. To calculate the optimal operation schedule, a mixed-integer linear optimization model is devised where the decision variables are settings of flexible appliances, charge/discharge status and amount of storage device, charge/discharge status, and amount of electrical vehicle. The devised optimization engine is linked to a database to acquire required data for optimization which encompasses historical data for customer load, forecasts of renewables, ratings of customers’ flexible appliances, and subjected energy tariff. The attained optimal scheduling for the customer is then returned to the database. On the other hand, the database is linked to the web-based user interface to get the user preferences (write to the database) and represent the recommendation for optimal operation and attained benefits (read from database). To manage the links between web-based user interface, database, and optimization tool, proper linking application programming interfaces (APIs) are devised. The proposed platform is testified using real-world data and its effectiveness is assured by experimental studies.

ACS Style

Saeed Teimourzadeh; Osman Bulent Tör; Mahmut Erkut Cebeci; Adela Bara; Simona Vasilica Oprea; Sabri Murat Kisakürek. Enlightening Customers on Merits of Demand-Side Load Control: A Simple-But-Efficient-Platform. IEEE Access 2020, 8, 1 -1.

AMA Style

Saeed Teimourzadeh, Osman Bulent Tör, Mahmut Erkut Cebeci, Adela Bara, Simona Vasilica Oprea, Sabri Murat Kisakürek. Enlightening Customers on Merits of Demand-Side Load Control: A Simple-But-Efficient-Platform. IEEE Access. 2020; 8 ():1-1.

Chicago/Turabian Style

Saeed Teimourzadeh; Osman Bulent Tör; Mahmut Erkut Cebeci; Adela Bara; Simona Vasilica Oprea; Sabri Murat Kisakürek. 2020. "Enlightening Customers on Merits of Demand-Side Load Control: A Simple-But-Efficient-Platform." IEEE Access 8, no. : 1-1.

Journal article
Published: 07 October 2020 in IEEE Access
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Currently, to meet the requirements of modern power systems as fully and efficiently as possible, the electricity markets have diversified greatly. Under these conditions, it becomes difficult for a producer to determine the structure of transactions that is financially optimal. Starting from the operational rules of the power systems that have shaped the electricity markets structure, the objective of this paper is to develop an electricity market simulator model that includes the basics of a best practice guide for producers that compete on various electricity markets to carry out the trading activities and enhance their financial results. The market simulator model considers both the bilateral long-or mid-term agreements and short-term offers on day-ahead, ancillary services and balancing markets providing the entire trading scenario and associated cash-flow and risks. Its significance consists in assisting the producer to plan its resources and create projections by performing multiple trading scenarios and selecting the best one. Thus, this paper proposes to uncover constraints and business rules for a simulator model assisting the market players to access the electricity markets and select the best option using Multiple-Criterial Decision-Making (MCDM) methods (Electre, Topsis, Analytical Hierarchy Process) or the weighted Euclidean distance. The simulations comprise four trading scenarios for different types of producers (gas or fossil-powered generators) generating 100 MW, that are ordered by independent criteria. The results obtained with MCDM and the proposed method showed that they indicated the same scenario as the best trading option based on the type of the producer.

ACS Style

Simona-Vasilica Oprea; Adela Bara; Dan Preotescu; Ramona Ana Bologa; Lucian Coroianu. A Trading Simulator Model for the Wholesale Electricity Market. IEEE Access 2020, 8, 184210 -184230.

AMA Style

Simona-Vasilica Oprea, Adela Bara, Dan Preotescu, Ramona Ana Bologa, Lucian Coroianu. A Trading Simulator Model for the Wholesale Electricity Market. IEEE Access. 2020; 8 (99):184210-184230.

Chicago/Turabian Style

Simona-Vasilica Oprea; Adela Bara; Dan Preotescu; Ramona Ana Bologa; Lucian Coroianu. 2020. "A Trading Simulator Model for the Wholesale Electricity Market." IEEE Access 8, no. 99: 184210-184230.

Journal article
Published: 29 July 2020 in Sustainability
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Electricity generation from renewable energy sources (RES) has a common feature, that is, it is fluctuating, available in certain amounts and only for some periods of time. Consuming this electricity when it is available should be a primary goal to enhance operation of the RES-powered generating units which are particularly operating in microgrids. Heavily influenced by weather parameters, RES-powered systems can benefit from implementation of sensors and fuzzy logic systems to dynamically adapt electric loads to the volatility of RES. This study attempts to answer the following question: How to efficiently integrate RES to power systems by means of sustainable energy solutions that involve sensors, fuzzy logic, and categorization of loads? A Smart Adaptive Switching Module (SASM) architecture, which efficiently uses electricity generation of local available RES by gradually switching electric appliances based on weather sensors, power forecast, storage system constraints and other parameters, is proposed. It is demonstrated that, without SASM, the RES generation is supposed to be curtailed in some cases, e.g., when batteries are fully charged, even though the weather conditions are favourable. In such cases, fuzzy rules of SASM securely mitigate curtailment of RES generation by supplying high power non-traditional storage appliances. A numerical case study is performed to demonstrate effectiveness of the proposed SASM architecture for a RES system located in Hulubești (Dâmbovița), Romania.

ACS Style

Simona-Vasilica Oprea; Adela Bâra; Ștefan Preda; Osman Bulent Tor. A Smart Adaptive Switching Module Architecture Using Fuzzy Logic for an Efficient Integration of Renewable Energy Sources. A Case Study of a RES System Located in Hulubești, Romania. Sustainability 2020, 12, 6084 .

AMA Style

Simona-Vasilica Oprea, Adela Bâra, Ștefan Preda, Osman Bulent Tor. A Smart Adaptive Switching Module Architecture Using Fuzzy Logic for an Efficient Integration of Renewable Energy Sources. A Case Study of a RES System Located in Hulubești, Romania. Sustainability. 2020; 12 (15):6084.

Chicago/Turabian Style

Simona-Vasilica Oprea; Adela Bâra; Ștefan Preda; Osman Bulent Tor. 2020. "A Smart Adaptive Switching Module Architecture Using Fuzzy Logic for an Efficient Integration of Renewable Energy Sources. A Case Study of a RES System Located in Hulubești, Romania." Sustainability 12, no. 15: 6084.

Conference paper
Published: 28 July 2020 in Advances in Intelligent Systems and Computing
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Nowadays the electricity consumption optimization represents a big improvement point for the electricity supplier, but also for the consumers. Both sides can benefit from the progress of sensors and ICT technologies and gain benefits if an automatically process is put in place. Hence, in this paper, we propose an algorithm which will monitor the electricity consumption and provide optimizations for each consumer, all in real time. For accurate monitoring outputs and better computation, the algorithm will run into a smart grid environment, where smart meters, actuator and appliances can be found and easily integrated. The proposed solution will be deployed in an edge computing environment. This architectural decision will make the final implementation more performant and less costly.

ACS Style

Răzvan Cristian Marales; Adela Bâra; Simona-Vasilica Oprea. Edge Computing in Real-Time Electricity Consumption Optimization Algorithm for Smart Grids. Advances in Intelligent Systems and Computing 2020, 188 -197.

AMA Style

Răzvan Cristian Marales, Adela Bâra, Simona-Vasilica Oprea. Edge Computing in Real-Time Electricity Consumption Optimization Algorithm for Smart Grids. Advances in Intelligent Systems and Computing. 2020; ():188-197.

Chicago/Turabian Style

Răzvan Cristian Marales; Adela Bâra; Simona-Vasilica Oprea. 2020. "Edge Computing in Real-Time Electricity Consumption Optimization Algorithm for Smart Grids." Advances in Intelligent Systems and Computing , no. : 188-197.

Journal article
Published: 08 May 2020 in TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES
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ACS Style

Osman Bülent Tör; Mahmut Erkut Cebeci; Mehmet Koç; Saeed Teimourzadeh; Deren Atli; Simona Vasilica Oprea; Adela Bara. Peak shaving and technical loss minimization in distribution grids: a time-of-use-based pricing approach for distribution service tariffs. TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 2020, 28, 1386 -1404.

AMA Style

Osman Bülent Tör, Mahmut Erkut Cebeci, Mehmet Koç, Saeed Teimourzadeh, Deren Atli, Simona Vasilica Oprea, Adela Bara. Peak shaving and technical loss minimization in distribution grids: a time-of-use-based pricing approach for distribution service tariffs. TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES. 2020; 28 (3):1386-1404.

Chicago/Turabian Style

Osman Bülent Tör; Mahmut Erkut Cebeci; Mehmet Koç; Saeed Teimourzadeh; Deren Atli; Simona Vasilica Oprea; Adela Bara. 2020. "Peak shaving and technical loss minimization in distribution grids: a time-of-use-based pricing approach for distribution service tariffs." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 28, no. 3: 1386-1404.

Journal article
Published: 06 May 2020 in Computers in Industry
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Nowadays, plenty of data is continuously pouring from the PhotoVoltaic Power Plants (PV) monitoring systems and sensors that could be successfully handled by big data technologies. This paper proposes a methodology that automatically collects the data logs from sensors installed on PV arrays, inverters and weather stations, checks the health status of the PV components, forecasts the generated power for each inverter based on its real operating conditions and the predicted irradiance and finally provides useful insights of the PV system based on the Key Performance Indicators (KPI) using big data technologies. The Ultra-Short-Term Forecast (USTF) algorithm provides the estimations of irradiance and generated power for the next 30 min and is applied on a sliding time window interval. The algorithm uses a Feed-Forward Artificial Neural Network (FF-ANN) and, to significantly reduce the number of iterations, we propose a backtracking adjustment of the learning rate that enables faster convergence reducing the computational time that is essential for USTF. Two data sets from PV Agigea 0.5 MW and PV Giurgiu 7.5 MW, located in the South-East and South of Romania, that consist in data logs from inverters and arrays, are used for simulation. The exhaustive analyses are performed for PV Agigea (including KPI calculation), while PV Giurgiu data set was mainly used to check the scalability and replicability of the algorithm.

ACS Style

Simona-Vasilica Oprea; Adela Bâra. Ultra-short-term forecasting for photovoltaic power plants and real-time key performance indicators analysis with big data solutions. Two case studies - PV Agigea and PV Giurgiu located in Romania. Computers in Industry 2020, 120, 103230 .

AMA Style

Simona-Vasilica Oprea, Adela Bâra. Ultra-short-term forecasting for photovoltaic power plants and real-time key performance indicators analysis with big data solutions. Two case studies - PV Agigea and PV Giurgiu located in Romania. Computers in Industry. 2020; 120 ():103230.

Chicago/Turabian Style

Simona-Vasilica Oprea; Adela Bâra. 2020. "Ultra-short-term forecasting for photovoltaic power plants and real-time key performance indicators analysis with big data solutions. Two case studies - PV Agigea and PV Giurgiu located in Romania." Computers in Industry 120, no. : 103230.

Journal article
Published: 23 April 2020 in Sustainability
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The smart metered electricity consumption data and high dimensional questionnaires provide useful information for designing the tariffs aimed at reducing electricity consumption and peak. The volume of data generated by smart meters for a sample of around four thousand residential consumers requires Not only Structured Query Language (NoSQL) solutions, data management and artificial neural network clustering algorithms, such as Self-Organizing Maps. In this paper, we propose a novel methodology that handles a large volume of data and extracts information from electricity consumption measured at 30 min and from complex questionnaires. Five three-level Time-of-Use tariffs are altered and investigated to minimize the consumers’ payment. Then, input data analysis revealed that the peak consumption is influenced by a segment of consumers that can be targeted to flatten the peak. Based on simulations, more than 23% of the peak consumption can be reduced by shifting it from peak to off-peak hours.

ACS Style

Simona-Vasilica Oprea; Adela Bâra; Bogdan George Tudorică; Gabriela Dobrița (Ene). Sustainable Development with Smart Meter Data Analytics Using NoSQL and Self-Organizing Maps. Sustainability 2020, 12, 3442 .

AMA Style

Simona-Vasilica Oprea, Adela Bâra, Bogdan George Tudorică, Gabriela Dobrița (Ene). Sustainable Development with Smart Meter Data Analytics Using NoSQL and Self-Organizing Maps. Sustainability. 2020; 12 (8):3442.

Chicago/Turabian Style

Simona-Vasilica Oprea; Adela Bâra; Bogdan George Tudorică; Gabriela Dobrița (Ene). 2020. "Sustainable Development with Smart Meter Data Analytics Using NoSQL and Self-Organizing Maps." Sustainability 12, no. 8: 3442.

Journal article
Published: 27 January 2020 in IEEE Access
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The electricity consumption will continue to increase despite the overall efforts and tendencies of changing the old appliances to less energy intensive ones. The advancements of Electric Vehicles (EV) and public mobility, electric heating, and the abundance of smart appliances that enhance the comfort of modern life lead to an increasing consumption trend. On the other hand, prosumers raising the quota of distributed generation and storage capacity will balance the electricity consumption trend. These changes at the consumption and generation level lead to the necessity to increase the awareness and incentive the consumers’ behavior to flatten the consumption curve and improve the savings. Such objectives could be reached by properly setting the Time-of-Use (ToU) tariff rates to encourage the consumption at off-peak hours when the rates are lower and unstress the grid loading. In this paper, we propose a methodology for setting the Time-of-Use (ToU) tariff rates and peak/off-peak intervals using big data technologies and machine learning, and verify the assumptions considering the large volume of consumption data of over 4200 residential consumers recorded in a smart metering implementation trail period that took place in Ireland from January to December 2010. We calculate the contribution to the peak/off-peak of the total consumption and use it in setting the ToU tariff rates starting from the flat tariff. Then, the consumers’ sensitivity to tariff change from flat to ToU is considered to identify the consumption change. The results show that using ToU instead of flat tariff, the peak is reduced in average by 5 to 7.5% and annual savings are around 4%. Also, by clustering the consumers a better allocation of the tariffs is possible. Thus, clustering is proposed considering the importance of the tariff allocation in Demand Side Management (DSM).

ACS Style

Simona-Vasilica Oprea; Adela Bara. Setting the Time-of-Use Tariff Rates With NoSQL and Machine Learning to a Sustainable Environment. IEEE Access 2020, 8, 25521 -25530.

AMA Style

Simona-Vasilica Oprea, Adela Bara. Setting the Time-of-Use Tariff Rates With NoSQL and Machine Learning to a Sustainable Environment. IEEE Access. 2020; 8 (99):25521-25530.

Chicago/Turabian Style

Simona-Vasilica Oprea; Adela Bara. 2020. "Setting the Time-of-Use Tariff Rates With NoSQL and Machine Learning to a Sustainable Environment." IEEE Access 8, no. 99: 25521-25530.

Journal article
Published: 09 December 2019 in IEEE Access
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In this paper, we propose a scalable Big Data framework that collects the data from smart meters and weather sensors, pre-processes and loads it into a NoSQL database that is capable to store and further process large volumes of heterogeneous data. Then, a set of Machine Learning (ML) algorithms are designed and implemented to determine the load profiles and forecast the electricity consumption for residential buildings for the next 24 hours. For the Short-Term Load Forecast (STLF), a Feed-Forward Artificial Neural Network (FF-ANN) algorithm with backtracking adjustment of the learning rate that extends and optimizes the Nesterov learning method is proposed. Its performance is compared with six algorithms, i.e. FF-ANN with well-known learning methods, namely Momentum and Nesterov, Non-linear AutoRegressive with eXogenous (NARX), Deep Neural Network (DNN), Gradient Tree Boosting (GTB) and Random Forests (RF) that are competitive and powerful ML algorithms which have been successfully used for load forecast. Hence, for STLF, the seven algorithms are executed simultaneously and the best one is automatically selected considering its accuracy in terms of Root Mean Square Errors (RMSE). The proposed methodology contains the steps required to implement the Big Data framework, i.e. data pre-processing, transformation and loading, the configuration of the ML algorithms for dimensionality reduction, clustering, STLF with different algorithms from which the Best Performant Algorithm (BPA) is automatically selected to provide STLF for the next 24 hours. The methodology is ultimately tested considering a real case of a residential smart building.

ACS Style

Simona-Vasilica Oprea; Adela Bara. Machine Learning Algorithms for Short-Term Load Forecast in Residential Buildings Using Smart Meters, Sensors and Big Data Solutions. IEEE Access 2019, 7, 177874 -177889.

AMA Style

Simona-Vasilica Oprea, Adela Bara. Machine Learning Algorithms for Short-Term Load Forecast in Residential Buildings Using Smart Meters, Sensors and Big Data Solutions. IEEE Access. 2019; 7 ():177874-177889.

Chicago/Turabian Style

Simona-Vasilica Oprea; Adela Bara. 2019. "Machine Learning Algorithms for Short-Term Load Forecast in Residential Buildings Using Smart Meters, Sensors and Big Data Solutions." IEEE Access 7, no. : 177874-177889.

Journal article
Published: 02 July 2019 in Journal of Modern Power Systems and Clean Energy
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This paper deals with optimal scheduling of networked microgrids (NMGs) considering resilience constraints. The proposed scheme attempts to mitigate the damaging impacts of electricity interruptions by effectively exploiting NMG capabilities. A three-stage framework is proposed. In Stage 1, the optimal scheduling of NMGs is studied through determining the power transaction between the NMGs and upstream network, the output power of distributed energy resources (DERs), commitment status of conventional DERs as well as demand-side reserves. In Stage 2, the decisions made at Stage 1 are realized considering uncertainties pertaining to renewable generation, market price, power consumption of loads, and unintentional islanding of NMGs from the upstream network and resynchronization. Stage 3 deals with uncertainties of unintentional islanding of each MG from the rest of islanded NMGs and resynchronization. The problem is formulated as a mixed-integer linear programming problem and its effectiveness is assured by simulation studies.

ACS Style

Saeed Teimourzadeh; Osman Bulent Tor; Mahmut Erkut Cebeci; Adela Bara; Simona Vasilica Oprea. A three-stage approach for resilience-constrained scheduling of networked microgrids. Journal of Modern Power Systems and Clean Energy 2019, 7, 705 -715.

AMA Style

Saeed Teimourzadeh, Osman Bulent Tor, Mahmut Erkut Cebeci, Adela Bara, Simona Vasilica Oprea. A three-stage approach for resilience-constrained scheduling of networked microgrids. Journal of Modern Power Systems and Clean Energy. 2019; 7 (4):705-715.

Chicago/Turabian Style

Saeed Teimourzadeh; Osman Bulent Tor; Mahmut Erkut Cebeci; Adela Bara; Simona Vasilica Oprea. 2019. "A three-stage approach for resilience-constrained scheduling of networked microgrids." Journal of Modern Power Systems and Clean Energy 7, no. 4: 705-715.

Journal article
Published: 14 June 2019 in Computers & Industrial Engineering
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The electricity consumption optimization and the advanced tariff schemes are some of the key factors in the demand response programs, which target several aspects such as the minimization of the consumption peak, a reduced electricity bill, the reduction of the consumption, etc. By various incentives, the electricity consumers become more and more active, thus being able to change their consumption behavior as a consequence of the tremendous progress of IT&C and sensor technologies. In this paper, we propose four novel algorithms that are able to minimize the consumption peak for eleven complex smart homes with over three hundred appliances and eight roof-PhotoVoltaic (PV) systems connected to the Internet and smart meters which allow different resolutions for the consumption records. The four algorithms are executed in parallel and the best option is chosen for the day-ahead electricity consumption optimization, while the electricity generated by the PV systems is shared at the community level, further improving the results of the optimization algorithms. In addition, several Time-of-Use (ToU) tariffs are implemented to assess the electricity payment and verify the efficiency of the proposed algorithms.

ACS Style

Simona Vasilica Oprea; Adela Bâra; George Adrian Ifrim; Lucian Coroianu. Day-ahead electricity consumption optimization algorithms for smart homes. Computers & Industrial Engineering 2019, 135, 382 -401.

AMA Style

Simona Vasilica Oprea, Adela Bâra, George Adrian Ifrim, Lucian Coroianu. Day-ahead electricity consumption optimization algorithms for smart homes. Computers & Industrial Engineering. 2019; 135 ():382-401.

Chicago/Turabian Style

Simona Vasilica Oprea; Adela Bâra; George Adrian Ifrim; Lucian Coroianu. 2019. "Day-ahead electricity consumption optimization algorithms for smart homes." Computers & Industrial Engineering 135, no. : 382-401.

Journal article
Published: 14 January 2019 in IEEE Access
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Big data frameworks enable companies from various fields to build models that allow them to increase profit margins by improving decision making at different levels (middle management, senior management, board) or by attempting to boost sales by customizing consumers’ experience based on their history and feedback. Institutions and other entities also use big data coming from all kinds of sensors, data that can be used to detect, in real time or in retrospect, possible problems (e.g., frauds, malfunctions, supply shortages) or to identify patterns and trends. In this paper, we organize large volumes of community electricity consumption data coming from smart meters, smart plugs and other sensors, but also data regarding consumers’ preferences in order to assist them to dynamically optimize their electricity consumption. In this regard, we develop a novel optimization approach that re-schedules every fifteen-minutes the appliances for residential consumers to reduce both the consumption peaks and the payments at the community level. The consumers send their day-ahead schedule that is optimized and further implemented to some extent. Thus, we monitor the electricity consumption via sensors and smart meters and dynamically adjust the schedule in case the real consumption deviates from the optimized plan, considering appliances constraints and consumers’ preferences. Every fifteen minutes, the algorithm evaluates the differences between the optimized schedule and the actual consumption and controls the operation of the interruptible appliances to stick with the day-ahead schedule as much as possible.

ACS Style

Simona Vasilica Oprea; Adela Bara; Vlad Diaconita. Sliding Time Window Electricity Consumption Optimization Algorithm for Communities in the Context of Big Data Processing. IEEE Access 2019, 7, 13050 -13067.

AMA Style

Simona Vasilica Oprea, Adela Bara, Vlad Diaconita. Sliding Time Window Electricity Consumption Optimization Algorithm for Communities in the Context of Big Data Processing. IEEE Access. 2019; 7 (99):13050-13067.

Chicago/Turabian Style

Simona Vasilica Oprea; Adela Bara; Vlad Diaconita. 2019. "Sliding Time Window Electricity Consumption Optimization Algorithm for Communities in the Context of Big Data Processing." IEEE Access 7, no. 99: 13050-13067.