Optimizing time of use (ToU) electricity pricing in regulated market

Time-of-Use (ToU) electricity price for residential consumers is receiving lots of attention lately with the increment of smart meters usage among residential customers. ToU prices reflect the actual electricity cost and the rate is commonly set base on market price of electricity. Implementing ToU pricing system on a regulated electricity system such as in Malaysia is complicated due to non existence of electricity market. The electrical utility company or the regulator will need to determine the optimum ToU prices that would give the correct price signal so that customers will react accordingly. Many factors need to be considered such as impact on electricity generation cost, load profile, load elasticity and customers’ satisfaction. This paper presents an optimization method to estimate the optimum ToU prices for given electricity demand profile and demand elasticity. The presented method able to reduce the gap between peak and off-peak demand and ensure the estimated ToU prices are fairly proportionate among hours i.e. summation of rate increments (from the fixed price) is equal to the summation of rate decrements. A simple system is used as a case study to demonstrate the application of the optimization method presented

An effective time of use tariffs scheme for residential area in Malaysia

Time of Use (TOU) is a tariffs scheme that provides variable rate structure for electricity depending on time of day used. In Malaysia, currently, the TOU scheme is only available for industrial and commercial customers. However, the government has wanted to implement TOU scheme in residential area so that a better load profiling can be obtained in the power system. The changes from block tariff to TOU scheme might be increase the customers’ electricity bill. Besides that, the unsuitable TOU structure and pricing might also cause the utility to lose their profit. Therefore, the main objective of this research is to identify the suitable time structure and pricing that will give beneficial to the utility and customers. The recommendation on appropriate incentives along with the TOU pricing signals to encourage customers to have better management on their consumption will also being considered. Thus, the analysis is beginning with residential customers’ behavior modeling. Next, the information of total electricity bill paid by customers will be determined. Last but not least, the statistical analysis will be used to identify suitable TOU structure and pricing. Therefore, the outcome of this research is an effective TOU scheme that will give beneficial to residential customers (in term o f reduce electricity bill) and utility (in term o f profit)

Optimising Energy Flexibility of Boats in PV-BESS Based Marina Energy Systems

Implementation of alternative energy supply solutions requires the broad involvement of local communities. Hence, smart energy solutions are primarily investigated on a local scale, resulting in integrated community energy systems (ICESs). Within this framework, the distributed generation can be optimally utilised, matching it with the local load via storage and demand response techniques. In this study, the boat demand flexibility in the Ballen marina on Samsø—a medium-sized Danish island—is analysed for improving the local grid operation. For this purpose, suitable electricity tariffs for the marina and sailors are developed based on the conducted demand analysis. The optimal scheduling of boats and battery energy storage system (BESS) is proposed, utilising mixed-integer linear programming. The marina’s grid-flexible operation is studied for three representative weeks—peak tourist season, late summer, and late autumn period—with the combinations of high/low load and photovoltaic (PV) generation. Several benefits of boat demand response have been identified, including cost savings for both the marina and sailors, along with a substantial increase in load factor. Furthermore, the proposed algorithm increases battery utilisation during summer, improving the marina’s cost efficiency. The cooperation of boat flexibility and BESS leads to improved grid operation of the marina, with profits for both involved parties. In the future, the marina’s demand flexibility could become an essential element of the local energy system, considering the possible increase in renewable generation capacity—in the form of PV units, wind turbines or wave energy

Rational consumer decisions in a peak time rebate program

A rational behavior of a consumer is analyzed when the user participates in a Peak Time Rebate (PTR) mechanism, which is a demand response (DR) incentive program based on a baseline. A multi-stage stochastic programming is proposed from the demand side in order to understand the rational decisions. The consumer preferences are modeled as a risk-averse function under additive uncertainty. The user chooses the optimal consumption profile to maximize his economic benefits for each period. The stochastic optimization problem is solved backward in time. A particular situation is developed when the System Operator (SO) uses consumption of the previous interval as the household-specific baseline for the DR program. It is found that a rational consumer alters the baseline in order to increase the well-being when there is an economic incentive. As results, whether the incentive is lower than the retail price, the user shifts his load requirement to the baseline setting period. On the other hand, if the incentive is greater than the regular energy price, the optimal decision is that the user spends the maximum possible energy in the baseline setting period and reduces the consumption at the PTR time. This consumer behavior produces more energy consumption in total considering all periods. In addition, the user with high uncertainty level in his energy pattern should spend less energy than a predictable consumer when the incentive is lower than the retail price

Demand Response Management in Smart Grid Networks: a Two-Stage Game-Theoretic Learning-Based Approach

In this diploma thesis, the combined problem of power company selection and Demand Response Management in a Smart Grid Network consisting of multiple power companies and multiple customers is studied via adopting a distributed learning and game-theoretic technique. Each power company is characterized by its reputation and competitiveness. The customers who act as learning automata select the most appropriate power company to be served, in terms of price and electricity needs’ fulfillment, via a distributed learning based mechanism. Given customers\u27 power company selection, the Demand Response Management problem is formulated as a two-stage game theoretic optimization framework, where at the first stage the optimal customers\u27 electricity consumption is determined and at the second stage the optimal power companies’ pricing is calculated. The output of the Demand Response Management problem feeds the learning system in order to build knowledge and conclude to the optimal power company selection. A two-stage Power Company learning selection and Demand Response Management (PC-DRM) iterative algorithm is proposed in order to realize the distributed learning power company selection and the two-stage distributed Demand Response Management framework. The performance of the proposed approach is evaluated via modeling and simulation and its superiority against other state of the art approaches is illustrated

Optimización de tarifas de la energía eléctrica para una respuesta a la demanda por medio de programación lineal

This paper develops a tariff optimization model for demand response by analyzing all costs generated by each stage for delivery energy to final consumers, prioritizing the recovery of all costs associated with the system, transferring it to customers by rates for demand response system "real time pricing”, ARIMA models are used to estimate the possible demand that the system will have during the next day, with this demand an economic dispatch is made considering the costs for generation, transmission, distribution, commercialization, reliability and losses, optimal rates to be applied to different customers of different zones, is the purpose, these rates will be calculated daily and vary depending on the behavior of customer consumption in previous days and the historical data of the system, encouraging the change in electricity consumption for the benefit of customers and the system.En este documento se desarrolla un modelo de optimización de tarifas, para la respuesta a la demanda analizando los costos generados por cada etapa para la entrega de energía a los consumidores finales, priorizando la recuperación de costos asociados al sistema, transfiriéndolos a los clientes por medio de tarifas con el sistema de respuesta a la demanda “real time pricing”, apoyado en modelos ARIMA, se estimará la posible demanda que tendrá el sistema durante el siguiente día, con esta demanda se realiza un despacho económico considerando los costos por generación, trasmisión, distribución, comercialización, confiabilidad y pérdidas; siendo la finalidad el cálculo de tarifas óptimas a ser aplicadas a diferentes clientes de distintos estratos. Estas tarifas serán calculadas diariamente y varían dependiendo el comportamiento del consumo de los clientes en días anteriores y los datos históricos del sistema, fomentando el cambio en consumo eléctrico para beneficio de los clientes y del sistema

Optimal Home Energy Management System for Committed Power Exchange Considering Renewable Generations

This thesis addresses the complexity of SH operation and local renewable resources optimum sizing. The effect of different criteria and components of SH on the size of renewable resources and cost of electricity is investigated. Operation of SH with the optimum size of renewable resources is evaluated to study SH annual cost. The effectiveness of SH with committed exchange power functionality is studied for minimizing cost while responding to DR programs