We consider the problem of an aggregator attempting to learn customers' load flexibility models while implementing a load shaping program by means of broadcasting daily dispatch signals. We adopt a multi-armed bandit formulation to account for the stochastic and unknown nature of customers' responses to dispatch signals. We propose a constrained Thompson sampling heuristic, Con-TS-RTP, that accounts for various possible aggregator objectives (e.g., to reduce demand at peak hours, integrate more intermittent renewable generation, track a desired daily load profile, etc) and takes into account the operational constraints of a distribution system to avoid potential grid failures as a result of uncertainty in the customers' response. We provide a discussion on the regret bounds for our algorithm as well as a discussion on the operational reliability of the distribution system's constraints being upheld throughout the learning process.
Maintaining the stability of renewable-dominant power systems requires the procurement of virtual inertia services from non-synchronous resources (e.g., batteries, wind turbines) in addition to inertia traditionally provided by synchronous resources (e.g., thermal generators). However, the pricing of inertia provision has not been studied in a stochastic electricity market, where the uncertainty characteristics of renewable energy sources (RES) are considered. To fill in this research gap, this paper formulates a chance-constrained stochastic unit commitment model with inertia requirements and…
The roll-out of stochastic renewable energy sources (RES) undermines the efficiency of power system and market operations. This paper proposes an approach to derive electricity prices that internalize RES stochasticity. We leverage a chance-constrained AC Optimal Power Flow (CC AC-OPF) model, which is robust against RES uncertainty and is also aware of the resulting variability (variance) of the system state variables. Using conic duality theory, we derive and analyze energy and balancing reserve prices that internalize the risk of system limit violations and the variance of system state varia…
Optimizing production scheduling of steel plate hot rolling for economic load dispatch under time-of-use electricity pricing
Xiangtan University（湘潭大学）；Hunan Province Cooperative Innovation Center for Wind Power Equipment and Energy Conversion, College of Information Engineering, Xiangtan University（湘潭大学信息工程学院风力发电设备与能量转换湖南省协同创新中心）；Xiangtan Iron and Steel Corporation Ltd（湘潭钢铁集团有限公司）
Installing smart meters to publish real-time electricity rates has been controversial while it might lead to privacy concerns. Dispatched rates include fine-grained data on aggregate electricity consumption in a zone and could potentially be used to infer a household's pattern of energy use or its occupancy. In this paper, we propose Blowfish privacy to protect the occupancy state of the houses connected to a smart grid. First, we introduce a Markov model of the relationship between electricity rate and electricity consumption. Next, we develop an algorithm that perturbs electricity rates befo…
The problem of heat and electricity pricing in combined heat and power systems regarding the time scales of electricity and heat, as well as thermal energy quality, is studied. Based on the asynchronous coordinated dispatch of the combined heat and power system, an energy-grade double pricing mechanism is proposed. Under the pricing mechanism, the resulting merchandise surplus of the heat system operator at each heat dispatch interval can be decomposed into interpretable parts and its revenue adequacy can be guaranteed for all heat dispatch intervals. And the electric power system operator's r…
Sharing economy has disrupted many industries. We foresee that electricity storage systems could be the enabler for sharing economy in electricity sector, though its implementation is a delicate task. Unlike in the 2-tier Time-of-Use (ToU) pricing, where greedy arbitrage policy can achieve the maximal electricity bill savings, most existing ToU schemes consist of multiple tiers, which renders the arbitrage challenging. The difficulty comes from the hedging against multiple tiers and the coupling between the decisions across the day. In this work, we focus on designing the energy sharing mechan…
Recently, chance-constrained stochastic electricity market designs have been proposed to address the shortcomings of scenario-based stochastic market designs. In particular, the use of chance-constrained market-clearing avoids trading off in-expectation and per-scenario characteristics and yields unique energy and reserves prices. However, current formulations rely on symmetric control policies based on the aggregated system imbalance, which restricts balancing reserve providers in their energy and reserve commitments. This paper extends existing chance-constrained market-clearing formulations…
Electricity distribution networks that contain large photovoltaic solar systems can experience power flows between customers. These may create both technical and socio-economic challenges. This paper establishes how these challenges can be addressed through the combined deployment of Community-scale Energy Storage (CES) and local network tariffs. Our study simulates the operation of a CES under a range of local network tariff models, using current Australian electricity prices and current network prices as a reference. We assess the financial outcomes for solar and non-solar owning customers a…
Electricity supply is not simply a matter of quantity, but a time lasting service that matches with a wave-like load curve. It logically deserves a pricing based on the curve per se rather than simply integral of load. This paper introduces to price electricity consumption based on dynamism of load, which is an equivalent characterization of load. Orthonormal basis with definable and distinguishable periodicity that can linearly express load curves constitutes a space to capture the dynamism, where coefficients of the basis quantify the dynamism in multiple dimensions. A price function is prop…
Price-based demand response (DR) of heating, ventilating, and air-conditioning (HVAC) systems is a challenging task, requiring comprehensive models to represent the building thermal dynamics and game theoretic interactions among participants. This paper proposes an online learning-based strategy for a distribution system operator (DSO) to determine optimal electricity prices, considering the optimal DR of HVAC systems in commercial buildings. An artificial neural network (ANN) is trained with building energy data and represented using an explicit set of linear and nonlinear equations, without …
Ambitious renewable portfolio standards motivate the incorporation of energy storage resources (ESR) as sources of flexibility. While the United States government aims to promote ESR participation in electricity markets, work on market designs for properly remunerating ESRs is still lacking. In this paper, we propose a new energy market clearing framework that incorporates ESR systems. The new market design is computationally attractive in that it avoids mixed-integer formulations and formulations with complementarity constraints. Moreover, compared to previous market designs, our market decom…
The implementation of electricity markets based on locational marginal pricing in a multi-settlement process has allowed wholesale competition, with pricing mechanisms that incentivize the optimal allocation of generation, transmission, and demand-response resources. While efficiency and reliability gains have been achieved in the US at a regional level, the lack of adequate inter-regional coordination mechanisms has limited broader gains. The shortcomings of cross-border coordination of electricity markets become more apparent as the industry transitions towards higher penetration of renewabl…
Tsinghua University（清华大学）；Institute for Interdisciplinary Information Sciences（交叉信息研究院）
Data analytics and machine learning techniques are being rapidly adopted into the power system, including power system control as well as electricity market design. In this paper, from an adversarial machine learning point of view, we examine the vulnerability of data-driven electricity market design. More precisely, we follow the idea that consumer's load profile should uniquely determine its electricity rate, which yields a clustering oriented pricing scheme. We first identify the strategic behaviors of malicious users by defining a notion of disguising. Based on this notion, we characterize…
Zonal pricing is a well-suited mechanism to incentivize grid-supporting behavior of profit-maximizing producers and consumers operating on a large-scale power system. In zonal electricity markets, local system operators create individual price zones, which provide appropriate price signals depending on local grid conditions such as an excess or shortage of electrical energy in certain regions. In this paper, a real-time zonal pricing controller for AC power networks is presented that ensures frequency and voltage stability as well as Pareto efficiency of the resulting closed-loop equilibria. B…
Fraunhofer Institute for Telecommunications, HHI（弗劳恩霍夫电信研究所）
As the share of renewable energy sources in the present electric energy mix rises, their intermittence proves to be the biggest challenge to carbon free electricity generation. To address this challenge, we propose an electricity pricing agent, which sends price signals to the customers and contributes to shifting the customer demand to periods of high renewable energy generation. We propose an implementation of a pricing agent with a reinforcement learning approach where the environment is represented by the customers, the electricity generation utilities and the weather conditions.
We propose a scenario-oriented approach for energy-reserve joint procurement and pricing for electricity market. In this model, without the empirical reserve requirements, reserve is procured according to all possible contingencies and load/renewable generation fluctuations with the minimum expected system total cost. The innovative locational marginal pricing approach for loads, generations and reserve and the associated settlement process are proposed. We show that payments from loads, payments to generators and congestion rent will reach their balance in the basecase as well as in all scena…
Modeling Coincident Peak Pricing in Electricity Markets: Challenges and Peak Shaving Effectiveness
John A. Paulson School of Engineering and Applied Sciences, Harvard University（哈佛大学约翰·A·保尔森工程与应用科学学院）；College, the University of Chicago（芝加哥大学学院）；Harvard Kennedy School of Government, Harvard University（哈佛大学肯尼迪政府学院）
Electric power generation, transmission, and distribution systems are attracting a large amount of interest from researchers with the development of the smart grid technologies. A smart grid aims at effective control and conditioning of the distribution of electricity. Pricing signal based distribution system are seen as one of the novel ways to achieve control in a smart grid. In our work, we propose to use a pricing signal modeled after the locational marginal price in the transmission system to locally provide price data to the users. The formulation and implementation of the distributional…
Incentive programs and ongoing reduction in costs are driving joint installation of solar PV panels and storage systems in residential households. There is a need for optimal investment decisions to reduce the electricity consumption costs of the households further. In this paper, we first develop analytical expression of storage investment decision and then of solar investment decision for a household which is under net metering billing mechanism with time of use pricing condition. Using real data of a residential household in Austin, TX, USA, we study how the investment decisions would provi…
Towards the Interactive Effects of Demand Response Participation on Electricity Spot Market Price
Energy Production and Infrastructure Center (EPIC)（能源生产与基础设施中心）；Belk College of Business（贝尔克商学院）；University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）
Electricity usage is a major portion of utility bills and the best place to start lowering them. An effective home energy management approach is introduced to decrease customers' electricity bills by determining the optimal appliance scheduling under hourly pricing based demand response (DR) strategies. The proposed approach specifically addresses consumer comfort through acceptable appliance deferral times, within which their consumption can be shifted from the normal schedules. Electric vehicles (EVs), with vehicle to grid (V2G) and vehicle-to-home (V2H) abilities, Energy storage systems (ES…
Time-of-use (ToU) pricing is widely used by the electricity utility. A carefully designed ToU pricing can incentivize end-users' energy storage deployment, which helps shave the system peak load and reduce the system social cost. However, the optimization of ToU pricing is highly non-trivial, and an improperly designed ToU pricing may lead to storage investments that are far from the social optimum. In this paper, we aim at designing the optimal ToU pricing, jointly considering the social cost of the utility and the storage investment decisions of users. Since the storage investment costs are …
Dynamic Pricing in Smart Grids under Thresholding Policies: Algorithms and Heuristics
Masdar Institute of Science and Technology（马斯达尔理工学院）；Brigham Young University（杨百翰大学）；New York University, Abu Dhabi（纽约大学阿布扎比分校）；Massachusetts Institute of Technology（麻省理工学院）
Time-of-use (ToU) pricing is widely used by the electricity utility to shave peak load. Such a pricing scheme provides users with incentives to invest in behind-the-meter energy storage and to shift peak load towards low-price intervals. However, without considering the implication on energy storage investment, an improperly designed ToU pricing scheme may lead to significant welfare loss, especially when users over-invest the storage, which leads to new energy consumption peaks. In this paper, we will study how to design a social-optimum ToU pricing scheme by explicitly considering its impact…
We analyze pricing mechanisms in electricity markets with AC power flow equations that define a nonconvex feasible set for the economic dispatch problem. Specifically, we consider two possible pricing schemes. The first among these prices are derived from Lagrange multipliers that satisfy Karush-Kuhn-Tucker conditions for local optimality of the nonconvex market clearing problem. The second is derived from optimal dual multipliers of the convex semidefinite programming (SDP) based relaxation of the market clearing problem. Relationships between these prices, their revenue adequacy and market e…
A Fair, Flexible, Zero-Waste Digital Electricity Market: A First-Principles Approach Combining Automatic Market Making, Holarchic Architectures and Shapley Theory
