Demand Side Energy Management Using Hybrid Chicken Swarm and Bacterial Foraging Optimization Techniques

This study uses a linear optimization framework to evaluate the effect of different demand response (DR)/load control mechanisms on reduction in peak load and energy consumption from the electricity grid in a home with four major controllable appliances — HVAC (heating, ventilation, and air-conditioning) systems, electric water heaters (EWHs), electric vehicles (EVs), and pool pumps (PPs). Two incentive-based DR methods and four price-based DR schemes — real time pricing (RTP), time-of-use (TOU) rates, critical peak prices (CPP), and variable peak prices (VPP) — are analyzed. Load reduction potential is evaluated for scenarios where the home has both onsite solar and storage, only solar, and no solar or storage. Results show that, from the utility’s perspective, the optimal load control schemes, which result in greatest reduction in peak load and energy consumption from the grid during peak hours, are CPP and VPP (critical price option). By considering the combined effect of demand response, solar generation, and energy storage systems, this study aims to equip electric utilities with the ability to make decisions about dynamic rate design and direct load control to curtail peak demand and shift energy usage.

The surging demand for electricity, fueled by environmental concerns, economic considerations, and the integration of distributed energy resources, underscores the need for innovative approaches to smart home energy management. This research introduces a novel optimization algorithm that leverages electric vehicles (EVs) as integral components, addressing the intricate dynamics of household load management. The study’s significance lies in optimizing energy consumption, reducing costs, and enhancing power grid reliability. Three distinct modes of smart home load management are investigated, ranging from no household load management to load outages, with a focus on the time-of-use (ToU) tariff impact, inclining block rate (IBR) pricing, and the combined effect of ToU and IBR on load management outcomes. The algorithm, a multi-objective approach, minimizes the peak demand and optimizes cost factors, resulting in a 7.9% reduction in integrated payment costs. Notably, EVs play a pivotal role in load planning, showcasing a 16.4% reduction in peak loads and a 7.9% decrease in payment expenses. Numerical results affirm the algorithm’s adaptability, even under load interruptions, preventing excessive increases in paid costs. Incorporating dynamic pricing structures like inclining block rates alongside the time of use reveals a 7.9% reduction in payment costs and a 16.4% decrease in peak loads. In conclusion, this research provides a robust optimization framework for smart home energy management, demonstrating economic benefits, peak load reduction potential, and enhanced reliability through strategic EV integration and dynamic pricing.

Implementing peak load reduction algorithms for household electrical appliances

An integrated price- and incentive-based demand response program for smart residential buildings: A robust multi-objective model

As electric vehicles (EVs) have being rapidly deployed into the transportation industry, they can be used for a reduction in peak load of electric power distribution systems. Therefore, the objective of this paper is to present a typical method that analyzes the effect of charging and discharging EVs on (a) a reduction in peak load, (b) an increase in under- (e.g., 0.95 PU) and over-voltage, (e.g., 1.05 PU), and (c) intermittency in voltage and total power generation. For this purpose, this paper (a) proposes a simple strategy that charges EVs at night and discharges them to the grid at peak demand hours, (b) defines a load factor and a change in total power generation from an interval to another, and (c) models a distribution system that hosts charging stations for EVs and photovoltaic (PV) systems. Then, to calculate the power flow of such a distribution system, including PV and charging systems, in steady state, this paper uses the Open Distribution Simulator (OpenDSS), a power system analysis computer program useful for calculating the power flow of electric power systems. Finally, it analyzes the effect of charging and discharging EVs on a distribution system.

The large-scale penetration of photovoltaic power generation systems (PVSs) may cause both the positive and negative impact on the existing electric power system. For the detail impact assessment, the prediction of the future electricity load pattern as well as the power output pattern of PVSs is very important, while it would be very challenging issue. As a first step of the impact assessment, this study estimates the change in the load curve on the utility power plant due to the large-scale penetration of PVS, by using the past data of both the total electricity load on the utility power plants and the global horizontal irradiance observed at 40 sites in Japan. Then, this study discusses the impact on the existing electric power system due to the large-scale penetration of PVSs, focusing on the reduction in annual peak and minimum load, and the change in time-zone of daily peak load and daily minimum load.

In this work, the Genetic Algorithm is explored for solving a predictive based demand side management problem (a combinatorial optimization problem) and the main measures for performance evaluation are evaluated. In this context, we propose a smart energy scheduling approach for household appliances in real-time to achieve minimum consumption costs and a reduction in peak load. We consider a scenario of self-consumption where the surplus from local power generation can be sold to the grid, and the existence of appliances that can be shiftable from peak hours to off-peak hours. Results confirm the importance of the tuning procedure and the structure of the genome and algorithm's operators determine the performance of such type of meta-heuristics. This fact is more decisive when there are several operational constraints on the system, as for example short-term optimal scheduling decision, time constraints and power limitations. Details about the scheduling problem, comparison strategies, metrics, and results are provided.

A new bio-inspired algorithm, Chicken Swarm Optimization (CSO), is proposed for optimization applications. Mimicking the hierarchal order in the chicken swarm and the behaviors of the chicken swarm, including roosters, hens and chicks, CSO can efficiently extract the chickens’ swarm intelligence to optimize problems. Experiments on twelve benchmark problems and a speed reducer design were conducted to compare the performance of CSO with that of other algorithms. The results show that CSO can achieve good optimization results in terms of both optimization accuracy and robustness. Future researches about CSO are finally suggested.

Peak demand is a major challenge for power utilities across the world. Demand Response (DR) is considered to be effective in addressing peak demand by altering consumption of end consumers, so as to match supply capability. However, an efficient DR system needs to respect end consumer convenience and understand their propensity of participating in a particular DR event, while altering the consumer demand. Understanding such preferences is non-trivial due to the large-scale and variability of consumers and the infrastructure changes required for collecting essential (smart meter and/or appliance specific) data. In this paper, we propose an inclusive DR system, iDR, that helps an electricity provider to design an effective demand response event by analyzing its consumers’ house-level consumption (smart meter) data and external context (weather conditions, seasonality etc.) data. iDR combines analytics and optimization to determine optimal power consumption schedules that satisfy an electricity provider’s DR objectives - such as reduction in peak load - while minimizing the inconvenience caused to consumers associated with alteration in their consumption patterns. iDR uses a novel context-specific approach for determining end consumer baseline consumptions and user convenience models. Using these consumer specific models and past DR experience, iDR optimization engine identifies -(i) when to execute a DR event, (ii) who are the consumers to be targeted for the DR, and (iii) what signals to be sent. Some of iDR’s capabilities are demonstrated using real-world house-level as well as appliance-level data.

Digital twin-driven architecture for AIoT-based energy service provision and optimal energy trading between smart nanogrids

Sierra Leone struggles with electricity with a 38% electrification rate. The expanding distribution network with limited generation capacity leads to frequent power outages during peak hours. Demand response (DR), a widely recognized demand‐side management strategy, has effectively balanced the electricity supply and generation worldwide. The primary objective of this study is to assess the potential for DR in the residential electricity sector, focusing on drivers such as security, environmental concerns, and incentives. The case study was conducted in Freetown, Sierra Leone. This study employs diversified demand modeling with household energy audit data to estimate the potential reduction in peak load achievable through voluntary demand response (VDR). This study generated load profiles for 12 household appliances and an aggregated load profile for the Freetown distribution network. Notably, the study achieved a substantial peak load reduction of 4.19 and 3.54 MW during the morning and evening peak hours, respectively. Additionally, this research highlights the significant roles of power security, price incentives, and environmental factors in encouraging residential consumers in Sierra Leone to participate in DR programs. This study underscores the response’s role in Sierra Leone’s electricity challenges, providing insights into peak load reduction and the motivation for residential consumer participation in DR programs.

A peak-load-reduction-based procedure to manage distribution network expansion by applying process-oriented costing of incoming components

An increasing share of electric vehicles can mean excessive peak loads in low‐voltage power distribution networks. Introducing peak shaving mechanisms to the charging systems, such overloads can be mitigated significantly. The first contribution of this study is to quantify the amount of flexibility that electric vehicles can contribute to peak load reduction so that the drivers can still fully charge the batteries of their vehicles. The second contribution is that the study presents and compares two optimisation strategies for peak load reduction. The work is based on real charging data covering about 25,000 charging sessions at various charging sites in the metropolitan area of the Finnish capital city. The main finding is that the peak loads at charging sites can be reduced by up to 55%. Another important result is that load reduction through low‐power charging is achievable only if the average parking time at the charging site is >3 h, without affecting the user experience negatively. It is also found out that the average parking time is over 2 h longer than the average charging time, which indicates the enormous potential of electric vehicles in peak shaving.

Floating Wind Energy farms are the next frontier is exploitation of offshore wind resources in open seas with high load factors. One of the challenges facing energy generation at these farms is the relatively high Levelized Cost of Electricity (LCOE). A typical wind farm is a complex installation with platform mounted turbine generators with multiple subsystems and strict requirements for station-keeping in face of wind and hydrodynamic loads. Mooring systems used for station keeping are subjected to high loads due to the environmental conditions. Hence, it is imperative to find cost-effective mooring solutions to lower the LCOE. Such solutions include the use of Mooring Optimization Technologies (MOTs) aimed at reducing the high peak and snatch loads in mooring lines and thus driving down the capital, operations and maintenance costs. In this paper, a wave tank testing campaign is described and analyzed. The objective of this testing campaign was to demonstrate the efficacy of an MOT for an open-source model wind platform in various water depths and sea states. The campaign produced high fidelity data that demonstrates that the selected MOT can achieve a peak load reduction of as much as 80% compared to a baseline without the MOT with large reductions in snatch load scenarios. Moreover, no significant impact on platform motion and nacelle acceleration for the open-source platform is observed, which implies the energy generation remains unaffected. These results, combined with simple construction and operation of the selected MOT, show great potential for certification and adoption of the technology for floating wind platforms.

The present paper introduces the Bacterial Foraging Optimization (BFO) technique to develop an efficient forecasting model for prediction of various stock indices. The connecting weights of the adaptive linear combiner based model are optimized by the BFO so that its mean square error(MSE) is minimized. The short and long term prediction performance of the model is evaluated with test data and the results obtained are compared with those obtained from the multilayer perceptron (MLP) based model. It is in general observed that the proposed model is computationally more efficient, prediction wise more accurate and takes less training time compared to the standard AMP based model.