Energy Storage as Public Asset

Energy storage has exhibited great potential in providing flexibility in power system to meet critical peak demand and thus reduce the overall generation cost, which in turn stabilizes the electricity prices. In this work, we exploit the opportunities for the independent system operator (ISO) to invest and manage storage as public asset, which could systematically provide benefits to the public. Assuming a quadratic generation cost structure, we apply parametric analysis to investigate the ISO's problem of economic dispatch, given variant quantities of storage investment. This investment is beneficial to users on expectation. However, it may not necessarily benefit everyone. We adopt the notion of marginal system cost impact (MCI) to measure each user's welfare and show its relationship with the conventional locational marginal price. We find interesting convergent characteristics for MCI. Furthermore, we perform $k$-means clustering to classify users for effective user profiling and conduct numerical studies on both prototype and IEEE test systems to verify our theoretical conclusions

Risk-limiting Economic Dispatch for Electricity Markets with Flexible Ramping Products

The expected increase in the penetration of renewables in the approaching decade urges the electricity market to introduce new products - in particular, flexible ramping products - to accommodate the renewables' variability and intermittency. CAISO and MISO are leading the design of the new products. However, it is not clear how such products may affect the electricity market. In this paper, we are specifically interested in assessing how the new products distort the optimal energy dispatch by comparing with the case without such products. The distortion may impose additional cost, which we term as the "distortion cost". Using a functional approach, we establish the relationship between the distortion cost and the key parameters of the new products, i.e., the up and down flexible ramping requirements. Such relationship yields a novel routine to efficiently construct the functions, which makes it possible to efficiently perform the minimal distortion cost energy dispatch while guaranteeing a given supply reliability level. Both theoretical analysis and simulation results suggest that smartly selecting the parameters may substantially reduce the distortion cost. We believe our approach can assist the ISOs with utilizing the ramping capacities in the system at the minimal distortion cost

Market Power in Convex Hull Pricing

The start up costs in many kinds of generators lead to complex cost structures, which in turn yield severe market loopholes in the locational marginal price (LMP) scheme. Convex hull pricing (a.k.a. extended LMP) is proposed to improve the market efficiency by providing the minimal uplift payment to the generators. In this letter, we consider a stylized model where all generators share the same generation capacity. We analyze the generators' possible strategic behaviors in such a setting, and then propose an index for market power quantification in the convex hull pricing schemes

Optimal Storage Control for Dynamic Pricing

Renewable energy brings huge uncertainties to the power system, which challenges the traditional power system operation with limited flexible resources. One promising solution is to introduce dynamic pricing to more consumers, which, if designed properly, could enable an active demand side. To further exploit flexibility, in this work, we seek to advice the consumers an optimal online control policy to utilize their storage devices facing dynamic pricing. Towards designing a more adaptive control policy, we devise a data-driven approach to estimating the price distribution. Simulation studies verify the optimality of our proposed schemes

Effective End-to-End Learning Framework for Economic Dispatch

Conventional wisdom to improve the effectiveness of economic dispatch is to design the load forecasting method as accurately as possible. However, this approach can be problematic due to the temporal and spatial correlations between system cost and load prediction errors. This motivates us to adopt the notion of end-to-end machine learning and to propose a task-specific learning criteria to conduct economic dispatch. Specifically, to maximize the data utilization, we design an efficient optimization kernel for the learning process. We provide both theoretical analysis and empirical insights to highlight the effectiveness and efficiency of the proposed learning framework

Rule Designs for Optimal Online Game Matchmaking

Online games are the most popular form of entertainment among youngsters as well as elders. Recognized as e-Sports, they may become an official part of the Olympic Games by 2020. However, a long waiting time for matchmaking will largely affect players' experiences. We examine different matchmaking mechanisms for 2v2 games. By casting the mechanisms into a queueing theoretic framework, we decompose the rule design process into a sequence of decision making problems, and derive the optimal mechanism with minimum expected waiting time. We further the result by exploring additional static as well as dynamic rule designs' impacts. In the static setting, we consider the game allows players to choose sides before the battle. In the dynamic setting, we consider the game offers multiple zones for players of different skill levels. In both settings, we examine the value of choice-free players. Closed form expressions for the expected waiting time in different settings illuminate the guidelines for online game rule designs

Optimal Electricity Storage Sharing Mechanism for Single Peaked Time-of-Use Pricing Scheme

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 mechanism for single peaked ToU scheme. To solve the problem, we identify that it suffices to understand the arbitrage policies for two forms of 3-tier ToU schemes. We submit that under mild conditions, the sharing mechanism yields a unique equilibrium, which supports the maximal social welfare

Storage Control for Carbon Emission Reduction: Opportunities and Challenges

Storage is vital to power systems as it provides the urgently needed flexibility to the system. Meanwhile, it can contribute more than flexibility. In this paper, we study the possibility of utilizing storage system for carbon emission reduction. The opportunity arises due to the pending implementation of carbon tax throughout the world. Without the right incentive, most system operators have to dispatch the generators according to the merit order of the fuel costs, without any control for carbon emissions. However, we submit that storage may provide necessary flexibility in carbon emission reduction even without carbon tax. We identify the non-convex structure to conduct storage control for this task and propose an easy to implement dynamic programming algorithm to investigate the value of storage in carbon emission reduction

A Data-driven Storage Control Framework for Dynamic Pricing

Dynamic pricing is both an opportunity and a challenge to the demand side. It is an opportunity as it better reflects the real time market conditions and hence enables an active demand side. However, demand's active participation does not necessarily lead to benefits. The challenge conventionally comes from the limited flexible resources and limited intelligent devices in demand side. The decreasing cost of storage system and the widely deployed smart meters inspire us to design a data-driven storage control framework for dynamic prices. We first establish a stylized model by assuming the knowledge and structure of dynamic price distributions, and design the optimal storage control policy. Based on Gaussian Mixture Model, we propose a practical data-driven control framework, which helps relax the assumptions in the stylized model. Numerical studies illustrate the remarkable performance of the proposed data-driven framework.Comment: arXiv admin note: text overlap with arXiv:1911.0696

Vulnerability Analysis for Data Driven Pricing Schemes

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 the sensitivity zones to evaluate the percentage of malicious users in each cluster. Based on a thorough cost benefit analysis, we conclude with the vulnerability analysis