Demand Response Pricing Research Articles

Economic optimization of smart distribution networks considering real-time pricing

With the development of smart meters, a real-time pricing (RTP) demand response is becoming possible for households in distribution networks. The power flow can be bidirectional in distribution networks which become smarter with distributed generators (DGs). It is expensive to import electricity from the generation far from load centers because of the cost of power loss and network use, so that it is more economical to use electricity generated by local distributed generators. Therefore, in order to curtail operating costs of distribution networks, this paper proposes a model of economic optimization conducted by distribution network operators. The electricity purchasing costs for distribution network operators are minimized by optimizing electric power from transmission systems and local distributed generators. Further, based on price elasticity, the formulations of load demand considering RTP are proposed with economic optimization of distribution networks. The economic optimization problems are resolved by an interior point method. The case study shows that peak load demand can be reduced about 3.5% because the household RTP and electricity purchasing costs of distribution network operators can save 28.86 £ every hour.

Potential Analysis of Demand Response Based on Peak and Valley Time Price

Tou price is based on the price as the guidance, guiding power users according to their own electricity utilization way of flexibility and interest adjustment behavior to realize the purpose of peak peel and improve the load rate. This paper is on the basis of considering the user satisfaction, using plastic load capacity and power generation side coal saving benefit after two measures of implementing peak valley price demand response potential quantitative analysis, and feasibility of implementing peak valley price for a specific region. At last, a big industrial user in a certain area as an example for analysis, the result shows that the implementation of peak valley price can bring potential benefits, and provide a guide that the feasibility of implementing peak valley price in the region.

Convergence of Volatile Power Markets With Price-Based Demand Response

Price-based demand response (DR) program is a mechanism for encouraging electricity consumers to dynamically manage their energy consumptions in response to time-varying electricity prices, and thereby reduce peak electricity demands and alleviate the pressure to power systems. However, it brings additional dynamics and new challenges to the real-time supply and demand balance. Specifically, if real-time price based DR programs are widely deployed in the future, price-sensitive DR load levels would constantly change in response to dynamic real-time prices, which will impact the economic dispatch (ED) schedule and in turn affect electricity market clearing prices. This paper adopts two methods for examining the impacts of different DR price elasticity characteristics and DR participation levels on the convergence of volatile power market: a closed-loop iterative simulation method and a non-iterative method based on the contraction mapping theorem. In this paper, convergence refers to the fact that load and/or price values will finally converge to a fixed point after a finite number of iterations between the ED problem and the price-sensitive DR load adjustment. Five price-sensitive DR performance function categories are used to simulate different nonlinear price elasticity characteristics of DR loads. Numerical studies illustrate how the convergence status of power markets will be affected by the nonlinear price elasticity DR curves, the DR penetration levels, and the capacity limits of generating units.

Real-Time Pricing for Demand Response Based on Stochastic Approximation

In this paper, we propose a new pricing algorithm to minimize the peak-to-average ratio (PAR) in aggregate load demand. The key challenge that we seek to address is the energy provider's uncertainty about the impact of prices on users' load profiles, in particular when users are equipped with automated energy consumption scheduling (ECS) devices. We use an iterative stochastic approximation approach to design two real-time pricing algorithms based on finite-difference and simultaneous perturbation methods, respectively. We also propose the use of a system simulator unit (SSU) that employs approximate dynamic programming to simulate the operation of the ECS devices and users' price-responsiveness. Simulation results show that our proposed real-time pricing algorithms reduce the PAR in aggregate load and help the users to reduce their energy expenses.

BioPSTM: a formal model for privacy, security, and trust in template-protecting biometric authentication

This paper presents a formal model, namely Biometric Privacy-Security-Trust Model BioPSTM, aiming to describe the tradeoff between privacy and security and their relationship with trust in biometric authentication systems. The relationship between trust and privacy-security pair requires a comprehensive approach that should consider user acceptance and the pricing between privacy and security. The proposed model is quite new in that it combines the formal formulation of tradeoff between privacy and security with trust over a user's acceptance model. The formal model presents a three-dimensional approach to indicate demand responsive pricing between privacy, security, and trust. The model is interpreted over a general syndrome-based biometric template protection method by discussing possible privacy and security requirements. The proposed model has been applied on countries that are aware of biometric security technologies. The evaluation on country profiles presents an overall description of the user acceptance model and its relationship with biometric technologies. Copyright © 2012 John Wiley & Sons, Ltd.

Optimum residential load management strategy for real time pricing (RTP) demand response programs

This paper presents an optimal load management strategy for residential consumers that utilizes the communication infrastructure of the future smart grid. The strategy considers predictions of electricity prices, energy demand, renewable power production, and power-purchase of energy of the consumer in determining the optimal relationship between hourly electricity prices and the use of different household appliances and electric vehicles in a typical smart house. The proposed strategy is illustrated using two study cases corresponding to a house located in Zaragoza (Spain) for a typical day in summer. Results show that the proposed model allows users to control their diary energy consumption and adapt their electricity bills to their actual economical situation.

Designing time-of-use program based on stochastic security constrained unit commitment considering reliability index

Recently in electricity markets, a massive focus has been made on setting up opportunities for participating demand side. Such opportunities, also known as demand response (DR) options, are triggered by either a grid reliability problem or high electricity prices. Two important challenges that market operators are facing are appropriate designing and reasonable pricing of DR options.In this paper, time-of-use program (TOU) as a prevalent time-varying program is modeled linearly based on own and cross elasticity definition. In order to decide on TOU rates, a stochastic model is proposed in which the optimum TOU rates are determined based on grid reliability index set by the operator. Expected Load Not Supplied (ELNS) is used to evaluate reliability of the power system in each hour. The proposed stochastic model is formulated as a two-stage stochastic mixed-integer linear programming (SMILP) problem and solved using CPLEX solver. The validity of the method is tested over the IEEE 24-bus test system. In this regard, the impact of the proposed pricing method on system load profile; operational costs and required capacity of up- and down-spinning reserve as well as improvement of load factor is demonstrated. Also the sensitivity of the results to elasticity coefficients is investigated.

Responsive pricing modeled with Stackelberg game for next-generation networks

In this paper, we study the problem of resource allocation between users in next-generation networks. The starting assumption is that the service provider tries to come up with convincing offers of service level to subscribers, based on demand responsive pricing scheme, while trying to maximize its profit. We developed two algorithms with different usage-based pricing strategies. Both algorithms solve pricing optimization problem using Stackelberg game with service provider acting as a leader and users behaving as followers. We developed the bandwidth management server to perform automatic optimal bandwidth allocation to each user's session and maximize its expected utility while maximizing the overall service provider's revenue. For both algorithms, we also performed the procedure for optimization of the capacity offered to users.

Time-of-use pricing and electricity demand response: evidence from a sample of Italian industrial customers

The introduction of real time pricing in many wholesale market as well as the liberalisation process involving the retail market poses the attention over the measurement of demand response to time differentiated price signals. This paper shows an example of how to estimate elasticities of substitution across time using a sample of Italian industrial customers facing time-of-use (TOU) pricing schemes. The model involves the estimation of a nested constant elasticity of substitution (CES) input demand function, which allows estimating substitutability of electricity usage across hourly intervals within a month and across different months.

Demand-Responsive Pricing Method for the Product Line of Taiwan High-Speed Rail

Presented is a demand-responsive pricing method for the product line of Taiwan high-speed rail. Considered are the objectives of both the operator and the passenger with their leader-follower and noncooperative relationships. The method is constructed by a bilevel program. A pricing model, which is the upper-level problem, considers three structures: basic model, market differential model, and product differential model. A demand model, which is the lower-level problem, considers service choice of passengers as a logit choice function where market competition is represented by a linear demand function and service capacity is used as a constraint for each service section. In a numerical study of three origin-destination pairs, presented are the results of the pricing models and their associated demand patterns. Convergence and sensitivity of model parameters are analyzed as well. The case study shows promising results for the flexibility and capability of the demand-responsive pricing method.