Refine
Institute
Keywords
- Maschinelles Lernen (14)
- Machine learning (11)
- OA-Publikationsfonds2020 (8)
- Deep learning (5)
- OA-Publikationsfonds2018 (4)
- big data (4)
- machine learning (4)
- Biodiesel (2)
- Internet of things (2)
- OA-Publikationsfonds2019 (2)
- artificial intelligence (2)
- artificial neural networks (2)
- clustering (2)
- data science (2)
- extreme learning machine (2)
- mathematical modeling (2)
- random forest (2)
- wireless sensor networks (2)
- ANN modeling (1)
- Algorithmus (1)
- Artificial Intelligence (1)
- Bildanalyse (1)
- Bodentemperatur (1)
- Bubble column reactor (1)
- ContikiMAC (1)
- Data Mining (1)
- ELM (1)
- Electric Energy Consumption (1)
- Energieeffizienz (1)
- Erneuerbare Energien (1)
- Fotovoltaik (1)
- Funktechnik (1)
- Gaussian process regression (1)
- Gebäude (1)
- Geometrie (1)
- Größenverhältnis (1)
- Hydrological drought (1)
- IOT (1)
- Infrastructures (1)
- Internet der Dinge (1)
- Internet der dinge (1)
- Internet of Things (1)
- K-nearest neighbors (1)
- KNN (1)
- Körper (1)
- Künstliche Intelligenz (1)
- M5 model tree (1)
- Machine Learning (1)
- Mensch (1)
- Neuronales Netz (1)
- Optimierung (1)
- Prediction (1)
- RSSI (1)
- Renewable energy (1)
- Risikomanagement (1)
- Sensor (1)
- Solar (1)
- Sustainability (1)
- Sustainable production (1)
- Vernetzung (1)
- action recognition (1)
- adaptive neuro-fuzzy inference system (ANFIS) (1)
- ant colony optimization algorithm (ACO) (1)
- artificial neural networks (ANN) (1)
- back-pressure (1)
- biodiesel (1)
- classification (1)
- classifier (1)
- clear channel assessments (1)
- cluster density (1)
- cluster shape (1)
- computation (1)
- computational fluid dynamics (CFD) (1)
- congestion control (1)
- coronary artery disease (1)
- demand response programs (1)
- diesel engines (1)
- dimensionality reduction (1)
- duty-cycles (1)
- energy efficiency (1)
- energy, exergy (1)
- ensemble model (1)
- estimation (1)
- extreme pressure (1)
- firefly optimization algorithm (1)
- flow pattern (1)
- fog computing (1)
- food informatics (1)
- forecasting (1)
- forward contracts (1)
- fuzzy decision making (1)
- growth mode (1)
- health informatics (1)
- heart disease diagnosis (1)
- human blob (1)
- human body proportions (1)
- hybrid machine learning (1)
- hybrid machine learning model (1)
- hydraulic jump (1)
- hydrology (1)
- image processing (1)
- industry 4.0 (1)
- least square support vector machine (LSSVM) (1)
- longitudinal dispersion coefficient (1)
- neural networks (NNs) (1)
- photovoltaic-thermal (PV/T) (1)
- precipitation (1)
- prediction (1)
- predictive model (1)
- principal component analysis (1)
- received signal strength indicator (1)
- reinforcement learning (1)
- response surface methodology (1)
- retailer (1)
- rice (1)
- risk management (1)
- rivers (1)
- rule based classification (1)
- seasonal precipitation (1)
- signal processing (1)
- smart sensors (1)
- soil temperature (1)
- spatial analysis (1)
- spatiotemporal database (1)
- spearman correlation coefficient (1)
- standard deviation of pressure fluctuations (1)
- statistical coeffcient of the probability distribution (1)
- stilling basin (1)
- stochastic programming (1)
- sugarcane (1)
- support vector machine (1)
- support vector machine (SVM) (1)
- support vector regression (1)
- water quality (1)
- wavelet transform (1)
- wireless sensor network (1)
Following restructuring of power industry, electricity supply to end-use customers has undergone fundamental changes. In the restructured power system, some of the responsibilities of the vertically integrated distribution companies have been assigned to network managers and retailers. Under the new situation, retailers are in charge of providing electrical energy to electricity consumers who have already signed contract with them. Retailers usually provide the required energy at a variable price, from wholesale electricity markets, forward contracts with energy producers, or distributed energy generators, and sell it at a fixed retail price to its clients. Different strategies are implemented by retailers to reduce the potential financial losses and risks associated with the uncertain nature of wholesale spot electricity market prices and electrical load of the consumers. In this paper, the strategic behavior of retailers in implementing forward contracts, distributed energy sources, and demand-response programs with the aim of increasing their profit and reducing their risk, while keeping their retail prices as low as possible, is investigated. For this purpose, risk management problem of the retailer companies collaborating with wholesale electricity markets, is modeled through bi-level programming approach and a comprehensive framework for retail electricity pricing, considering customers’ constraints, is provided in this paper. In the first level of the proposed bi-level optimization problem, the retailer maximizes its expected profit for a given risk level of profit variability, while in the second level, the customers minimize their consumption costs. The proposed programming problem is modeled as Mixed Integer programming (MIP) problem and can be efficiently solved using available commercial solvers. The simulation results on a test case approve the effectiveness of the proposed demand-response program based on dynamic pricing approach on reducing the retailer’s risk and increasing its profit.
In this paper, the decision-making problem of the retailers under dynamic pricing approach for demand response integration have been investigated. The retailer was supposed to rely on forward contracts, DGs, and spot electricity market to supply the required active and reactive power of its customers. To verify the effectiveness of the proposed model, four schemes for retailer’s scheduling problem are considered and the resulted profit under each scheme are analyzed and compared. The simulation results on a test case indicate that providing more options for the retailer to buy the required power of its customers and increase its flexibility in buying energy from spot electricity market reduces the retailers’ risk and increases its profit. From the customers’ perspective also the retailers’accesstodifferentpowersupplysourcesmayleadtoareductionintheretailelectricityprices. Since the retailer would be able to decrease its electricity selling price to the customers without losing its profitability, with the aim of attracting more customers. Inthiswork,theconditionalvalueatrisk(CVaR)measureisusedforconsideringandquantifying riskinthedecision-makingproblems. Amongallthepossibleoptioninfrontoftheretailertooptimize its profit and risk, demand response programs are the most beneficial option for both retailer and its customers. The simulation results on the case study prove that implementing dynamic pricing approach on retail electricity prices to integrate demand response programs can successfully provoke customers to shift their flexible demand from peak-load hours to mid-load and low-load hours. Comparing the simulation results of the third and fourth schemes evidences the impact of DRPs and customers’ load shifting on the reduction of retailer’s risk, as well as the reduction of retailer’s payment to contract holders, DG owners, and spot electricity market. Furthermore, the numerical results imply on the potential of reducing average retail prices up to 8%, under demand response activation. Consequently, it provides a win–win solution for both retailer and its customers.