Heuristic Optimization of Consumer Electricity Costs Using a Generic Cost Model

Many new demand response strategies are emerging for energy management in smart grids. Real-Time Energy Pricing (RTP) is one important aspect of consumer Demand Side Management (DSM), which encourages consumers to participate in load scheduling. This can help reduce peak demand and improve power system efficiency. The use of Intelligent Decision Support Systems (IDSSs) for load scheduling has become necessary in order to enable consumers to respond to the changing economic value of energy across different hours of the day. The type of scheduling problem encountered by a consumer IDSS is typically NP-hard, which warrants the search for good heuristics with efficient computational performance and ease of implementation. This paper presents an extensive evaluation of a heuristic scheduling algorithm for use in a consumer IDSS. A generic cost model for hourly pricing is utilized, which can be configured for traditional on/off peak pricing, RTP, Time of Use Pricing (TOUP), Two-Tier Pricing (2TP) and combinations thereof. The heuristic greedily schedules controllable appliances to minimize smart appliance energy costs and has a polynomial worst-case computation time. Extensive computational experiments demonstrate the effectiveness of the algorithm and the obtained results indicate the gaps between the optimal achievable costs are negligible

Machine learning and data segmentation for building energy use prediction—a comparative study

Advances in metering technologies and emerging energy forecast strategies provide opportunities and challenges for predicting both short and long-term building energy usage. Machine learning is an important energy prediction technique, and is significantly gaining research attention. The use of different machine learning techniques based on a rolling-horizon framework can help to reduce the prediction error over time. Due to the significant increases in error beyond short-term energy forecasts, most reported energy forecasts based on statistical and machine learning techniques are within the range of one week. The aim of this study was to investigate how facility managers can improve the accuracy of their building’s long-term energy forecasts. This paper presents an extensive study of machine learning and data processing techniques and how they can more accurately predict within different forecast ranges. The Clarendon building of Teesside University was selected as a case study to demonstrate the prediction of overall energy usage with different machine learning techniques such as polynomial regression (PR), support vector regression (SVR) and artificial neural networks (ANNs). This study further examined how preprocessing training data for prediction models can impact the overall accuracy, such as via segmenting the training data by building modes (active and dormant), or by days of the week (weekdays and weekends). The results presented in this paper illustrate a significant reduction in the mean absolute percentage error (MAPE) for segmented building (weekday and weekend) energy usage prediction when compared to unsegmented monthly predictions. A reduction in MAPE of 5.27%, 11.45%, and 12.03% was achieved with PR, SVR and ANN, respectively

Application of Cost Benefits Analysis for the Implementation of Renewable Energy and Smart Solution Technologies: A Case Study of InteGRIDy Project †

Cost−benefit analysis is a common evaluation method applied to assess whether an energy system is economically feasible as well as the economic viability of energy investment for the energy transition of a pre-existing energy system. This paper focuses on examining the economic costs and benefits obtained through the implementation of renewable energy and smart technology to a pre-existing energy system of two pilot sites—St. Jean and Barcelona. The evaluation process includes all relevant parameters such as investment, operating and maintenance costs, and energy prices needed to assess the economic feasibility of the investment. The results show that investing in energy system development towards a decarbonized future, can provide various benefits such as increased flexibility, and reduced emissions while being economically feasible

Optimized Residential Loads Scheduling Based On Dynamic Pricing Of Electricity : A Simulation Study

This paper presents a simulation study which addresses Demand Side Management (DSM) via scheduling and optimization of a set of residential smart appliances under day-ahead variable pricing with the aim of minimizing the customer’s energy bill. The appliances’ operation and the overall model are subject to the manufacturer and user specific constraints formulated as a constrained linear programming problem. The overall model is simulated using MATLAB and SIMULINK / SimPowerSystems basic blocks. The results comparing Real Time Pricing (RTP) and the Fixed Time Tariff (FTT) demonstrate that optimal scheduling of the residential smart appliances can potentially result in energy cost savings. The extension of the model to incorporate renewable energy resources and storage system is also discussedNon peer reviewe

Cost-effective electrification of remote houses and communities with renewable energy sources

Hybrid energy systems play a critical role in the electrification of remote dwellings. Currently, many residents in remote areas have no connection to the electricity grid and rely on standalone power systems driven by diesel generators. The integration of hybrid renewable energy sources has become an attractive solution to enhance power system capabilities, cost savings and environmental performance in recent times and hence provides a potentially cleaner alternative to diesel generators. However, effective operation of hybrid alternatives in such ways requires the careful configuration and operation of multiple power sources. This paper explores a hybrid system that involves a combination of renewable generation, battery energy storage and a diesel backup generator as a non-renewable energy source to supply electricity to rural areas. Easy-to-apply analysis and design rules are developed and discussed, which when applied can ensure adequate capacity to cover loads while retaining cost-effective operations in such off-grid situations. A case study considered is for a remote village in Malar, in the Awaran District of Baluchistan province in Pakistan. The hybrid renewable energy system is designed using the specified procedures and the Homer-pro software and simulated to determine the optimal cost for various combinations of renewables under different cases in comparison to the use of diesel generator as a raw base case. Building Energy optimization (BEopt) software is used for the creation of representative load profiles that reflects the consumption pattern of the remote household dwellers under consideration. Extensive simulation results demonstrate the effectiveness of the hybrid system, and the obtained results indicate a significant reduction in energy cost post initial outlay. The effective cost of energy under the hybrid system of various combination of renewables is computed to be $0.171kWh, whereas the effective cost for the diesel generator system is$0.467kWh, resulting to 63% difference in energy cost. Similarly, this is reflective of the Net Present Cost (NPC) of Diesel Generator which is approximately thrice that of a hybrid system. The paper concludes by observing that even in the absence of financial instruments to support renewable and storage technology procurement and installation, design rules and configuration guidance tools such as that described in this work can increase the Rate of Return (ROR), and hence larger benefits for rural communities wishing to decarbonize and reduce long-term costs can be leveraged

Near-optimal scheduling of residential smart home appliances using heuristic approach

This paper presents an efficient heuristic approach for scheduling residential smart home appliances. Using available hourly prices for electricity, the starting times of a supplied set of appliances are optimized so that the economic cost of the energy consumed is reduced, while satisfying the operational and peak power constraints. The algorithm schedules appliances one after the other based on a greedy strategy. The heuristic (c.f. exact) approach is taken to reduce the computational burden to a level allowing re-optimization to take place at regular intervals by a modest computing device without specialized software, which could be embedded in a smart meter. The proposed algorithm is evaluated through a preliminary experimental study comparing the obtained costs and computation times with an exact algorithm. Results indicate that the obtained cost was within 5% of the optimal cost, while the computation time reduced by exponential factors.Peer reviewedFinal Accepted Versio

Heuristic scheduling of multiple smart home appliances: Utility planning perspective

Electric utilities are increasingly incorporating Demand Side Management (DSM) approaches in their energy networks to help compensate for increased levels of uncertainty arising from renewable energy production. Demand Response (DR) is one such approach. DR aims to encourage shifts in residential load by using pricing signals and dynamic tariff mechanisms which are provided in real-time by the utility company. The goal is to shift energy consumption patterns to off-peak times and hence reduce the Peak-to-Average Ratio (PAR) of the daily electricity demand. In this paper, the effects of multiple households using a fast heuristic algorithm for scheduling smart appliances is simulated from a utility planning perspective. It explores the aggregated response of the de-centralized heuristic algorithms to events signaled by the utility, when the primary focus of each heuristic is upon minimization of end-user economic costs. The performance of the heuristic algorithm for DR events under normal and stringent conditions is explored under simulation. Results confirm that the aggregated demand can potentially respond to DR signals, although the choice of price signals plays a major role in the depth and nature of the response and requires further investigation