Dynamic pricing

About: Dynamic pricing is a research topic. Over the lifetime, 4144 publications have been published within this topic receiving 91390 citations. The topic is also known as: surge pricing & demand pricing.

Dynamic Pricing Game Under Different Channel Power Structures in a Closed-Loop Supply Chain

Abstract: The importance of closed-loop supply chains has been widely recognized both in academic communities and in industrial sectors. This paper starts from the traditional supply chains and the new self-...

Price-Based Residential Demand Response Management in Smart Grids: A Reinforcement Learning-Based Approach

Abstract: This paper studies price-based residential demand response management (PB-RDRM) in smart grids, in which non-dispatchable and dispatchable loads (including general loads and plug-in electric vehicles (PEVs)) are both involved. The PB-RDRM is composed of a bi-level optimization problem, in which the upper-level dynamic retail pricing problem aims to maximize the profit of a utility company (UC) by selecting optimal retail prices (RPs), while the lower-level demand response (DR) problem expects to minimize the comprehensive cost of loads by coordinating their energy consumption behavior. The challenges here are mainly two-fold: 1) the uncertainty of energy consumption and RPs; 2) the flexible PEVs' temporally coupled constraints, which make it impossible to directly develop a model-based optimization algorithm to solve the PB-RDRM. To address these challenges, we first model the dynamic retail pricing problem as a Markovian decision process (MDP), and then employ a model-free reinforcement learning (RL) algorithm to learn the optimal dynamic RPs of UC according to the loads' responses. Our proposed RL-based DR algorithm is benchmarked against two model-based optimization approaches (i.e., distributed dual decomposition-based (DDB) method and distributed primal-dual interior (PDI)-based method), which require exact load and electricity price models. The comparison results show that, compared with the benchmark solutions, our proposed algorithm can not only adaptively decide the RPs through on-line learning processes, but also achieve larger social welfare within an unknown electricity market environment.

Design and Implementation of an Open, Interoperable Automated Demand Response Infrastructure

Abstract: Design and Implementation of an Open, Interoperable Automated Demand Response Infrastructure Mary Ann Piette, Sila Kiliccote and Girish Ghatikar Lawrence Berkeley National Laboratory Building 90-3111 Berkeley CA 94720 mapiette@lbl.gov, SKiliccote@lbl.gov, GGhatikar@lbl.gov Keywords: Demand response, automation, commercial buildings, peak demand Abstract This paper describes the concept for and lessons from the development and field-testing of an open, interoperable communications infrastructure to support automating demand response (DR). Automating DR allows greater levels of participation and improved reliability and repeatability of the demand response and customer facilities. Automated DR systems have been deployed for critical peak pricing and demand bidding and are being designed for real time pricing. The system is designed to generate, manage, and track DR signals between utilities and Independent System Operators (ISOs) to aggregators and end-use customers and their control systems. 1. INTRODUCTION California utilities have been exploring the use of critical peak pricing (CPP) and other DR pricing and program strategies to help reduce peak day summer time electric loads. Recent experience with DR has shown that customers have limited knowledge of how to operate their facilities to reduce their electricity costs under CPP or in a DR Program [1]. While the lack of knowledge about how to develop and implement DR control strategies is a barrier to participation in DR programs like CPP, another barrier is the lack of automation of DR systems. Most DR activities are manual and require building operations staff to first receive emails, phone calls, and pager signals, and second, to act on these signals to execute DR strategies. The various levels of DR automation can be defined as follows. Manual Demand Response involves a labor- intensive approach such as manually turning off or changing comfort set points at each equipment switch or controller. Semi-Automated Demand Response involves a pre- programmed demand response strategy initiated by a person via centralized control system. Fully-Automated Demand Response does not involve human intervention, but is initiated at a home, building, or facility through receipt of an external communications signal. The receipt of the external signal initiates pre-programmed demand response strategies. The authors refer to this as Auto-DR. One important concept in Auto-DR is that a homeowner or facility manager should be able to “opt out” or “override” a DR event if the event comes at time when the reduction in end-use services is not acceptable. From the customer side, modifications to the site’s electric load shape can be achieved by modifying end-use loads. Examples of demand response strategies include reducing electric loads by dimming or turning off non-critical lights, changing comfort thermostat set points, or turning off non- critical equipment. These demand response activities are triggered by specific actions set by the electricity service provider, such as dynamic pricing or demand bidding. Many electricity customers have suggested that automation will help them institutionalize their demand response. The alternative is manual demand response -- where building staff receives a signal and manually reduces demand. Lawrence Berkeley National Laboratory (LBNL) research has found that many building energy management and controls systems (EMCS) and related lighting and other controls can be pre-programmed to initiate and manage electric demand response. This paper provides an overview of the AutoDR field tests and implementation activities from 2003-2007. A companion paper describes the technology in greater detail. This paper focuses on the automation design history and does not cover the shed strategy or shed measurement details which are covered in previous papers [2,3,4,5]. 2. TECHNOLOGY HISTORY The automated demand response project began in 2002 following California’s electricity market crisis with the goal of addressing three key research questions. First, is it possible using today’s technology to develop a low-cost, fully automated infrastructure to improve DR capability in California? Second, how “ready” are commercial buildings to receive common signals? Third, once a building receives

Discontinuous Demand Functions: Estimation and Pricing

Abstract: We consider a dynamic pricing problem with an unknown and discontinuous demand function. There is a seller who dynamically sets the price of a product over a multiperiod time horizon. The expected ...

Efficient and scalable demand response for the smart power grid

Abstract: A demand response setup is considered entailing a set of appliances with deferrable and non-interruptible tasks. A mixed-integer linear programming model for scheduling the operational periods and power levels of the appliances is formulated in response to known dynamic pricing information with the objective of minimizing the total electricity cost and consumer dissatisfaction. A scalable algorithm yielding a near-optimal solution is developed by enforcing a separable structure, and using Lagrangian relaxation. Thus, the original problem is decomposed to per-appliance subproblems, which can be solved exactly based on dynamic programming. The proximal bundle method is employed to obtain a solution to the convexified version, which helps recovery of a primal feasible solution. Numerical tests validate the proposed approach.