Electricity Cost Savings Research Articles

Multi-objective optimization of household appliance scheduling problem considering consumer preference and peak load reduction

This paper addresses the load scheduling problem in residential sector while considering preferences of consumers and reduction of peak load. This study proposes an optimization model using multi-objective mixed integer linear programming considering a time-of-use (ToU) electricity tariff. Furthermore, this study considers the coordinated peak load reduction in a multiple-household environment. The proposed model aims to minimize three objectives: the electricity cost, the scheduling inconvenience and the peak load. Considering three objectives could enable consumers and utility companies to control their priority in minimizing one over the others. Three multi-objective optimization approaches are applied to solve the proposed model: normalized weighted-sum approach, preemptive optimization and compromise optimization. Numerical experiments show that the proposed solutions lead to significant savings in electricity costs, eliminate consumer inconvenience, while reducing the system peak loading. Furthermore, the results show outstanding performance when compared against three schedules from the literature and the consumer’s preferred schedule. Moreover, the coordinated schedules for the multiple-household problem lead to a significant reduction and levelling of the aggregated peak load.

Economic nonlinear model predictive control using hybrid mechanistic data-driven models for optimal operation in real-time electricity markets: In-silico application to air separation processes

Optimization of the energy consumption at fluctuating short-term electricity markets is a promising measure to increase the economic efficiency of energy-intense processes. This can be addressed by integrating the economic perspective directly into the process control, i.e., by using economic nonlinear model predictive control (eNMPC). We present a single-layer eNMPC framework for optimal operation of an industrial-scale nitrogen plant participating in real-time electricity markets. To achieve real-time capability, we utilize suboptimal updates as well as our reduced modeling approach for rectification columns combining compartmentalization and artificial neural networks (Schäfer et al., AIChE J., doi:10.1002/aic.16568). We demonstrate the real-time capability of the approach in-silico. We explicitly account for model-plant mismatch by using a detailed full-order stage-by-stage model that is common in literature as plant replacement. Our results show that close-to-optimal savings in electricity costs are enabled via the eNMPC strategy even under consideration of inherently uncertain market forecasts whilst safely satisfying production targets. Furthermore, the disturbance rejection capability of the control structure is investigated, showing that severe unmeasured disturbances with slow dynamics can be rejected effectively without violating product requirements.

An IoT-Based Data Driven Precooling Solution for Electricity Cost Savings in Commercial Buildings

The buildings sector is one of the largest energy consuming entities today, accounting for about 40% of global energy consumption. Alongside, electricity prices are increasing in several nations worldwide, putting pressure on facility managers to reduce the expense incurred in operating their buildings. In this paper, we focus on building heating, ventilation, and air conditioning and make the following contributions. First, inspired by the deployment of Internet of Things in buildings, we propose a data driven gray box model for zone thermal dynamics and use it to develop an optimization framework for precooling a building. The goal is to determine a precooling strategy that not only shifts the peak air conditioning power to low electricity tariff regimes, but also reduces the peak power, energy consumption, and electricity costs associated with cooling a building. Second, the optimal precooling problem turns out to be nonlinear. To enable ease of use of our framework in the real-world without undermining its efficacy, we develop a linearized version of the problem and show that it has comparable performance to the original formulation. Third, we quantify the benefits of the precooling optimization framework by applying it to building management system data, across multiple days, obtained from a large office building in Australia. The evaluations reveal that optimal precooling lowers the peak power, energy consumption, and electricity costs for cooling the building by up to 30% while ensuring that the thermal comfort of the occupants is maintained. We conclude by describing two applications of the proposed optimization framework for use in developing countries. Our precooling solution incurs no capital expense and permits facility managers to take decisions from a data driven point of view for improving the energy efficiency of their buildings.

Assessments of demand response potential in small commercial buildings across the United States

Buildings are responsible for more than 73% of the total electricity usage in the United States and have a significant contribution to peak electrical demand of the power grid. Thus, proper building demand management could improve the robustness and efficiency of grid operation and could also result in economic benefits for building owners. Almost half of the building electricity usage occurs in the commercial building sector where 60% of the installed cooling capacity is attributed to small commercial buildings employing packaged air-conditioning systems (e.g., rooftop units). Thus, understanding the demand response potential and economic benefits for the small commercial sector is of great importance. In particular, the effect of climate, building type, and utility structure on demand response potential is valuable information for building managers in identifying the most beneficial demand response strategies and for utilities in developing better utility structure rate incentives to achieve demand reduction. This article presents a simulation-based assessment approach and highlights the key assessment results for three demand response strategies—thermostat, shade, and lighting controls—across a range of building types, vintages, locations, and utility rate structures. The control performance is evaluated in terms of utility cost savings, peak demand reductions, and indoor comfort impacts in comparison to a baseline night setup strategy. The tradeoff between cost savings and comfort is also explicitly investigated. The assessment results show that the demand response controls lead to slightly worse indoor comfort compared to the baseline control, with an increase in the median percentage of people dissatisfied from 5.7% to 8.1%. However, significant demand reductions and cost savings could be achieved across all tested cases with the magnitudes dependent on the weather condition and building type. Annual electricity cost savings of 5 to 25 cents/ft2 (54 to 269 cents/m2) were demonstrated for time-of-use energy rates with demand charges, whereas the savings were relatively small, below 5 cents/ft2 (54 cents/m2) for most cases, when buildings subscribed to flat rates without demand charges.

A Hierarchical Optimisation of a Compressed Natural Gas Station for Energy and Fuelling Efficiency under a Demand Response Program

Compressed natural gas stations serve customers who have chosen compressed natural gas powered vehicles as an alternative to diesel and petrol based ones, for cost or environmental reasons. The interaction between the compressed natural gas station and electricity grid requires an energy management strategy to minimise a significant component of the operating costs of the station where demand response programs exist. Such a strategy when enhanced through integration with a control strategy for optimising gas delivery can raise the appeal of the compressed natural gas, which is associated with reduced criteria air pollutants. A hierarchical operation optimisation approach adopted in this study seeks to achieve energy cost reduction for a compressed natural gas station in a time-of-use electricity tariff environment as well as increase the vehicle fuelling efficiency. This is achieved by optimally controlling the gas dispenser and priority panel valve function under an optimised schedule of compressor operation. The results show that electricity cost savings of up to 60.08% are achieved in the upper layer optimisation while meeting vehicle gas demand over the control horizon. Further, a reduction in filling times by an average of 16.92 s is achieved through a lower layer model predictive control of the pressure-ratio-dependent fuelling process.

Stochastic optimal energy management system for RTG cranes network using genetic algorithm and ensemble forecasts

Abstract In low voltage networks, Energy Storage Systems (ESSs) play a significant role in increasing energy cost savings, peak reduction and energy efficiency whilst reinforcing the electrical network infrastructure. This paper presents a stochastic optimal management system based on a Genetic Algorithm (GA) for the control of an ESS equipped with a network of electrified Rubber Tyre Gantry (RTG) cranes. The stochastic management system aims to improve the reliability and economic performance, for given ESS parameters, of a network of cranes by taking into account the uncertainty in the RTGs electrical demand. A specific case study is presented using real operational data of the RTGs netwrok in the Port of Felixstowe, UK, and the results of the stochastic control system is compared to a standard set-point controller. In this paper, two forecast data sets with different levels of accuracy are used to investigate the impact of the crane demand forecast error in the proposed ESS control system. The results of the proposed control strategies indicate that the stochastic management system successfully increases the electric energy cost savings, the peak demand reductions and successfully outperforms a comparable set-point controller.

Comparison of New and Previous Net Energy Metering (NEM) Scheme in Malaysia

The introduction of new Net Energy Metering (NEM) scheme in 2016 in Malaysia is seen as an improvement from the previous FIT scheme. NEM allows a user to generate, use and export the net excess energy to the grid instead of selling all the generated energy as in FIT scheme. However, the NEM scheme (NEM 2016) still have a problem which is small size residential customers will not get benefited from the scheme in term of electricity bill savings. Therefore, the Malaysian government introducing a new NEM scheme (NEM 2019) to replace previous NEM scheme (NEM 2016) to overcome drawbacks of NEM 2016. This paper will investigate the performance of NEM 2019 overcome the previous problem by comparing the performance of NEM 2019 and against NEM 2016 in term of NPC (net present cost) and electricity cost savings by using HOMER software. The analysis is conducted on three different size of residential customers; small, medium and large with different PV panel sizes from 1kWp to 8kWp. The results show that NEM 2019 will produce lower NPC compared to NEM 2016 for most cases.

Price-responsive model predictive control of floor heating systems for demand response using building thermal mass

Floor heating (FH) system is a widely-used thermally active building system, which can take advantage of building thermal mass to shift energy demands to off-peak hours. Its ability of effective utilization of low-temperature energy resources also helps to increase energy efficiency and reduce greenhouse gas emissions. However, control of FH systems remains a challenge due to the large thermal inertia of the pipe-embedded concrete floor. In the context of smart grids, the control issue becomes more complicated because the dynamic electricity prices need to be taken into consideration to achieve economic benefits and encourage demand response participation. In this study, an advanced optimal control method, i.e., model predictive control (MPC), is developed for FH systems, which can simultaneously consider all the influential variables including weather conditions, occupancy and dynamic electricity prices. Considering the on-line computational efficiency, a control-oriented dynamic thermal model for a room integrated with FH system is developed and represented in a stochastic state-space form. An economic MPC controller, formulated as a mixed integer linear programming problem, is designed for FH systems. A TRNSYS-MATLAB co-simulation testbed is developed to test and compare different control methods under various operating conditions in terms of energy consumption, thermal comfort and operating costs. Test results show that, compared to the conventional on-off controller, the MPC controller is able to use building thermal mass to optimally shift energy consumption to low-price periods, improve thermal comfort at the beginning of occupancy, reduce energy demand during peak periods, and save electricity costs for residential end-users. The weather conditions and electricity prices have influences on the start-up time and duration of preheating, energy flexibility potential and electricity cost savings of FH systems.

Techno-economic analysis of flexible heat pump controls

There exists a gap between the flexibility needed within the German energy system and the flexibility being actually provided. Heat pumps can be seen to be the core technology to couple the heating and electricity sectors and thereby provide flexibility to the system. We contribute to the scientific discussion by conducting validated simulations of realistic and easily applicable heat pump control methods that are implemented to provide flexibility and not limited to pilot trials. We assess effects on energy efficiency and economic potentials compared to standard reference control algorithms. The flexible control schemes under investigation can be divided into two categories: time-of-use based controls and spot market price based control. We investigate both control types for current market conditions and for future scenarios of the German electricity market in 2030. In our study, we introduce a validated MODELICA simulation model of a ground source heat pump and market models to evaluate financial consequences. Our results indicate that efficiency losses occur for all investigated control methods. Further, financial gains for heat pump owners could not be achieved with current price structures, only distinct future scenarios with high shares of fluctuating renewables and low electricity demand led to enough price fluctuations to allow for electricity cost savings. Thus, we conclude that (1) heat pump owners need to weigh pros and cons of currently applicable, flexible heat pump controls carefully before their implementation, (2) there exists a need for heat pump manufacturers to further develop controllers that are capable of more advanced control schemes on large-scale, (3) policy makers should establish market mechanisms to ensure that heat pump owners are provided with incentives to use flexible controls, and (4) scientific modelers should not only focus on the development of highly elaborated control methods, but also investigate their transferability to large-scale real-world applications in the near future.

An Efficient Power Scheduling in Smart Homes Using Jaya Based Optimization with Time-of-Use and Critical Peak Pricing Schemes

Presently, the advancements in the electric system, smart meters, and implementation of renewable energy sources (RES) have yielded extensive changes to the current power grid. This technological innovation in the power grid enhances the generation of electricity to meet the demands of industrial, commercial and residential sectors. However, the industrial sectors are the focus of power grid and its demand-side management (DSM) activities. Neglecting other sectors in the DSM activities can deteriorate the total performance of the power grid. Hence, the notion of DSM and demand response by way of the residential sector makes the smart grid preferable to the current power grid. In this circumstance, this paper proposes a home energy management system (HEMS) that considered the residential sector in DSM activities and the integration of RES and energy storage system (ESS). The proposed HEMS reduces the electricity cost through scheduling of household appliances and ESS in response to the time-of-use (ToU) and critical peak price (CPP) of the electricity market. The proposed HEMS is implemented using the Earliglow based algorithm. For comparative analysis, the simulation results of the proposed method are compared with other methods: Jaya algorithm, enhanced differential evolution and strawberry algorithm. The simulation results of Earliglow based optimization method show that the integration of RES and ESS can provide electricity cost savings up to 62.80% and 20.89% for CPP and ToU. In addition, electricity cost reduction up to 43.25% and 13.83% under the CPP and ToU market prices, respectively.

Analisa Konsumsi Energi Listrik Rumah Dengan Kendali Otomatis

An automatic control of the home lights can save energy consumption, thereby reducing the cost of electricity payment. This paper aims to test the Cost Saving Energy Electricity tool with automatic control of home lighting using Arduino Mega 2560 microcontroller, because it has advantages that the number of input / output a lot so that the flexibility of the controlled light load can be more. The control system used a RTC (Real Time Clock) method, which has excess timer that can always be up-to-date. Also, the ACS-712 Current Sensor can measure the outflow current on the light load to be displayed by LCD and the Relay Module can control automaticly On/Off. However, problems experienced when using the On/Off switch contact manually that was turn on or off the light, which at the different clocks due to human negligence so that the power consumption being not well controlled. Test results that have been done for a month with a maximum load of 300 Watt was obtained electric power consumption for reading on kWh meter by using cost saving energy tool of 61.4 kWh while in the manual system of 85.7 kWh. Furthermore, it got the power difference of 24.3 kWh. The value of electricity cost savings of 35.655 (TDL, Rp 1,467.28 / kWh). Therefore, the use of cost saving energy to control home lighting automatically proved more energy efficient than the manual system using the On/Off switch.
 

Condensing boilers and variable speed pumps as a refurbishment option for heating systems in residential buildings - Energy and economic evaluation

This paper studies the energy and economic impacts from the replacement of a high-temperature hydronic heating system, consisting of an oil-fired conventional boiler and constant-speed circulator, by a low-temperature system with gas-fired condensing boiler and electronically controlled variable speed pump, in a residential building. Five case studies, which are categorized by the overall thermal insulation of the building and by the installed heating system, are examined in each of the two largest towns in Greece, namely Athens and Thessaloniki. In each study, the central heating system is designed and the thermal and electrical energy consumption is estimated by simulating the system according to the Bin method. Additionally, the operating cost, the cost of replacing the heating system, and the payback period of the investment are calculated. In the cases analysed, without any thermal insulation upgrade, the annual reduction of the thermal energy requirements varies from 26 to 29%. By upgrading the thermal insulation, thermal energy savings vary from 40 to 84%, depending on the previous insulation level of the building. With the use of variable speed circulators, electrical energy savings exceed 87% in all case studies. The fuel and electricity cost savings (prices of 2016) vary from 37 to 51%.

Battery aging in multi-energy microgrid design using mixed integer linear programming

This paper introduces a linear battery aging and degradation model to a multi-energy microgrid sizing model using mixed integer linear programming. The battery aging model and its integration into a larger microgrid sizing formulation are described. A case study is provided to explore the impact of considering battery aging on key results: optimal photovoltaic and storage capacities, optimal distributed energy resources operations strategies, and annual cost and generation metrics.The case study results suggest that considering battery degradation in optimal microgrid sizing problems significantly impacts the perceived value of storage. Depending on capacity loss and lifetime targets, considering battery degradation is shown to decrease optimal storage capacities between 6 and 92% versus scenarios that do not consider battery health. When imposing constant distributed energy resource capacities, inclusion of degradation can decrease optimal annual battery cycling by as much as a factor five and reduce total annual electricity cost savings from otherwise identical photovoltaic and storage systems by 5–12%. These results emphasize that as batteries grow in maturity and ubiquity for distributed energy applications, considering battery health and capacity loss is an essential component of any analytical tool or model to guide system planning and decision-making.

Optimal operation of photovoltaic/battery/diesel/cold-ironing hybrid energy system for maritime application

Due to emission regulations, the ship is sometimes restricted or even prohibited to use diesel when berthing in port, and the shore power (called the “cold-ironing”) takes part as power supplier. For a green ship with onboard photovoltaic (PV) systems, energy management for maritime PV/battery/diesel/cold-ironing hybrid energy system (HES) can significantly reduce the electricity cost of a ship. In this paper, the optimal operation of HES on a ship is modelled as an optimization problem subject to a number of constraints, including emission regulations of ports. Optimal control and model predictive control (MPC) methods are developed to dispatch the power flow when the ship is in port. Finally, the proposed approaches are tested by simulation experiments for different cases of cold-ironing service prices and emission regulations. Experimental results show that the optimal operation of maritime HES can bring promising electricity cost savings and robustness.

Adaptive Optimized Pattern Extracting Algorithm for Forecasting Maximum Electrical Load Duration Using Random Sampling and Cumulative Slope Index

Load forecasting techniques can be an essential method to save energy and shave peak loads in order to improve energy efficiency and maintain the stability of a power grid. To achieve this goal, machine learning-based approaches have been proposed recently. Before moving toward the long-term and ultimate solution such as machine learning, we propose a simple and efficient method to forecast electricity usage patterns and the duration of maximum electrical load using a small data set. The proposed algorithm can forecast maximum electrical load duration using random sampling and a cumulative slope index. To verify the algorithm, we utilized electricity data (from 2015.11 to 2016.12) obtained from a building with a constant lifestyle and electricity pattern. The performance of the algorithm was evaluated using electricity bills, the discharging condition of an energy storage system, and the cumulative slope index. It was found that the proposed algorithm could provide electricity cost savings of 0.62–2.28% compared with other, conventional electricity prediction techniques, such as the moving average method and exponential smoothing. In near future research, it is expected that this algorithm could be applied to electrical big data to handle real-time data processing.

A novel optimal energy-management strategy for a maritime hybrid energy system based on large-scale global optimization

In the hybrid energy system of large green ships, different types of energy sources are employed to feed the electricity demand. An optimal energy-management model and control methodology must be developed to obtain operational safety and efficiency. In this study, optimal power-flow dispatching of maritime photovoltaic/battery/diesel/cold-ironing hybrid energy systems is proposed to sufficiently explore solar energy and minimize the ship’s electricity cost. By modelling the constraints (such as power balance, solar output, diesel output, battery capacity, and regulations from the port) as penalty functions, the optimal energy-management is described as an unconstrained, large-scale, global optimization problem, which can be effectively solved by the proposed adaptive multi-context cooperatively coevolving particle swarm optimization algorithm. The proposed approach is verified by simulation for different cases. Results of the simulation show that the optimal energy-management of the evaluated system can be obtained with great electricity cost savings and robust control performance.

Application of methodology for the adequacy of the electrical motor's power sizing: permanent and transient analysis

Abstract The suitability of the motive force contributes to the efficient use of electrical energy. On the other hand, the inadequate size of electric motors is directly connected to increased investment and running costs. This article presents the theory and mainly the application of a methodology for the adequacy of the motive power. The research was conducted at the Federal University of Vicosa dairy factory. This methodology consists in measuring motor rotation, acquiring technical information from the manufacturer's manual, and identifying the type of motor load. In addition, it uses the linearization method to estimate the resistance torque in steady state. This step is achieved without the need for using additional equipment or sending the motor to a laboratory for measuring the torque. In this sense, the studies can be made during the production process. Thus, this relevant methodology has the advantage of allowing the studies to be carried out at the agro-industry facilities. After getting all the information described above, it was possible to determine whether each motor at the factory was oversized. Then, the suitable motor was selected according to the load type. The application of the methodology described herein could provide around 50.6% savings in the monthly electricity costs at the dairy factory, and an attractive internal return rate.

Design and development of a Building Façade Integrated Asymmetric Compound Parabolic Photovoltaic concentrator (BFI-ACP-PV)

Building Integrated PV and Concentrating PV can generate electricity onsite and provide savings in materials and electricity costs, as well as protecting buildings from weather. In this paper, a novel truncated stationary asymmetric compound parabolic photovoltaic concentrator with a geometric concentration ratio of 2.0 has been designed and experimental characterised. The designed system is suitable for building façade application, especially for vertical façade. It has wide acceptance half angles of 0° and 55°, this acceptance angle range enables the concentrator to operate year-round at its geometric gain in most of the UK and EU climatic condition. A comprehensive indoor test was carried out to evaluate the electrical and thermal characterisation of the developed Building Façade Integrated Asymmetric Compound Parabolic Photovoltaic concentrator (BFI-ACP-PV) system, and also the factors that affect the power output of the developed system. The experimental results showed that the developed BFI-ACP-PV system has the potential to increase the power output per unit solar cell area by a factor of 2, when compared with a non-concentrating PV system. Subsequently, a Phase Change Material (PCM) system was integrated to the rear of the BFI-ACP-PV system to moderate the PV temperature rise and maintain good solar to electrical conversion efficiency. It was found out that the electrical conversion efficiency for the BFI-ACP-PV coupled PCM system was increased by over 5% compared with a similar system with no PCM integrated at the rear, when the incident solar radiation intensity was 280 W/m2, this value increased by over 10% for an incident solar radiation intensity of 670 W/m2.

Organic Rankine Cycle-assisted ground source heat pump combisystem for space heating in cold regions

This paper presents an Organic Rankine Cycle (ORC)-assisted ground source heat pump combisystem for the cascaded utilization of low-grade energy and shallow geothermal energy in cold regions. The ORC unit was added to conventional ground source heat pumps to solve the cold accumulation problem, which can lead to performance degradation of heat pumps. The existing types and novel types of the combisystem were then modeled with the Transient System Simulation Program (TRNSYS). During the heating season, the ORC unit and heat pump unit were combined for space heating, and during the non-heating season, the ORC unit was connected to ground heat exchangers for seasonal storage. System performance was simulated over periods of one year and twenty years. The twenty-year simulation results showed that the proposed combisystem could maintain a higher annual average coefficient of performance (COP) of approximately 3.8 because of the steady soil temperature, whereas the annual average COP of the conventional ground source heat pump system decreased from 3.7 to 3.2. Additionally, the total power consumption per unit heating area of the heating system decreased from 2.2 × 103 kW·h/m2 to 3.3 × 102 kW·h/m2 when the ORC unit was added to the conventional ground source heat pump. This translates to electricity cost savings of ¥ 908/m2 over twenty years. Moreover, in the combisystem, the ORC unit provides 55.6% of the total heating capacity; compensates for 78.5% of the heat pump unit’s power consumption. And 94.1% of the thermal energy storage from the non-heating season can be used during the heating season by the heat pump unit.

A priority-induced demand side management system to mitigate rebound peaks using multiple knapsack

Demand side management (DSM) in smart grid authorizes consumers to make informed decisions regarding their energy consumption pattern and helps the utility in reducing the peak load demand during an energy stress time. This results in reduced carbon emission, consumer electricity cost, and increased grid sustainability. Most of the existing DSM techniques ignore priority defined by consumers. In this paper, we present priority-induced DSM strategy based on the load shifting technique considering various energy cycles of an appliance. The day-ahead load shifting technique proposed is mathematically formulated and mapped to multiple knapsack problem to mitigate the rebound peaks. The autonomous energy management controller proposed embeds three meta-heuristic optimization techniques; genetic algorithm, enhanced differential evolution, and binary particle swarm optimization along with optimal stopping rule, which is used for solving the load shifting problem. Simulations are carried out using three different appliances and the results validate that the proposed DSM strategy successfully shifts the appliance operations to off-peak time slots, which consequently leads to substantial electricity cost savings in reasonable waiting time, and also helps in reducing the peak load demand from the smart grid. In addition, we calculate the feasible regions to show the relationship between cost, energy consumption, and delay.