Jhi-Young Joo

Efficient Coordination of Wind Power and Price-Responsive Demand—Part I: Theoretical Foundations

In Part I of this two-part paper, we introduce several possible methods for integrating wind power, price-responsive demand and other distributed energy resources (DERs). These methods differ with respect to information exchange requirements, computational complexity, and physical implementability. A novel look-ahead interactive dispatch that internalizes inter-temporal constraints at the DERs level, and dispatches the results of distributed decisions subject to spatial security constraints, is proposed as a possible effective algorithm. This method requires only the use of todays static security-constrained economic dispatch (SCED) by the system operators. The optimization accounting for inter-temporal constraints, and ramping rates in particular, is done by the DERs while they create their own supply and demand functions. To implement this method, todays supervisory control and data acquisition (SCADA) needs to be transformed into a multi-directional, multi-layered information exchange system.

Multi-Layered Optimization Of Demand Resources Using Lagrange Dual Decomposition

Summary form only given. This paper concerns mathematical conditions under which a system-level optimization of supply and demand scheduling can be implemented as a distributed optimization in which users and suppliers, as well as the load serving entities, are decision makers with well-defined sub-objectives. We start by defining the optimization problem of the system that includes the sub-objectives of many different players, both supply and demand entities in the system, and decompose the problem into each players optimization problem, using Lagrange dual decomposition. A demand entity or a load serving entitys problem is further decomposed into problems of the many different end-users that the load serving entity serves. By examining the relationships between the global objectives and the local/individual objectives in these multiple layers and the optimality conditions of these decomposable problems, we define the requirements of these different objectives to converge. We propose a novel set of methods for coordinating supply and demand over different time horizons, namely day-ahead scheduling and real-time adjustment. We illustrate the ideas by simulating simple examples with different conditions and objectives of each entity in the system.

Efficient Coordination of Wind Power and Price-Responsive Demand—Part II: Case Studies

In Part II of this paper, we demonstrate on a modified 24-bus IEEE Reliability Test System (RTS) that it is indeed possible to manage large-scale intermittent resources in coordination with price-responsive demand. The algorithms introduced in Part I of this paper are simulated assuming 20% and 50% wind capacity. We show that the look-ahead dispatch of the proposed method is physically implementable and, when used with elastic demand, can accommodate the integration of close to 50% wind capacity. This is contrasted with the observation that, without elastic demand, 50% wind integration is not physically implementable. From the perspective of total market surplus, the predictive dispatch of the proposed method is 3.8% suboptimal when compared to the centralized most efficient predictive economic dispatch. Further simulations are needed to test the feasibility of coordinating wind power and price-responsive demand on realistic large-scale power systems.

A Decision-Making Framework and Simulator for Sustainable Electric Energy Systems

In this paper, we propose a new framework for the organization of the electric power industry, based on extensive use of information technology (IT) and on interactive decision making, where consumers and distributed producers join the traditional actors, utilities in particular, in making decisions. While many ideas considered in this paper have been put forward in recent years, such as the need to manage intermittency of renewable resources by means of proactive forecasting, and coordination with responsive demand and storage, we introduce a possible systematic IT-enabled mechanism necessary for the actual implementation of these technologies. We point out that in order to achieve a long-term sustainable energy utilization, it is essential to provide on-line information to internalize the value of just-in-time, just-in-place, and just-in-context distributed adaptation across the entire supply chain, ranging from the smallest consumers and energy providers, through their aggregators and system coordinators. We illustrate using our model-based novel simulator, how a carefully designed multidirectional and multitemporal information exchange could enable sustainable decision making while accounting for unique needs and capabilities of various resources and users. At the same time, information incentivizes the resources and users to contribute to system-wide sustainability objectives at value. We illustrate the dependence of such decisions-driven industry evolution on the industry rules (choice of performance objectives), as well as on the operating and planning practices for implementing the industry rules (temporal and spatial factors). Our model-based simulator could be used as a means of designing novel regulation defining rules, rights, and responsibilities regarding the type and rate of information to be exchanged in support of sustainable industry evolution.

Option Valuation Applied to Implementing Demand Response via Critical Peak Pricing

The purpose of this paper is to examine the economic and technical perspectives of critical peak pricing plan as an active demand response(DR) program. To implement a good DR program, there are three perspectives to be considered: regulatory, economic, and technical perspectives. This paper will assume that the regulatory perspective of DR is determined as critical peak pricing(CPP) plan and examine the other two. The economic perspective of CPP plan is the incentive of the plan conductor, or the profit of an energy service provider(ESP). The technical perspective is a method to maximize the incentive of CPP plan, or an ESPs profit. An ESP should decide when to call critical peaks within certain constraints to maximize her profit. This is done by predicting the market prices and following a similar method as evaluating a swing option. The numerical example will show the optimal critical peak decisions.

A possible engineering and economic framework for implementing demand side participation in frequency regulation at value

In this paper we propose an extension of todays Automatic Generation Control (AGC) to enable the participation of demand in frequency stabilization and regulation. This is in addition to the demand participation in energy markets. We stress that as many intermittent energy resources get deployed, it becomes necessary to account for their fluctuations much the same way as the AGC accounts for load fluctuations at present. We outline both technical and economic approach to implementing AGDC at value. A simple example is used to illustrate proof-of-concept for AGDC.

Economic Dispatch for an Agent-Based Community Microgrid

In this paper, an economic dispatch (ED) problem for a community microgrid is studied. In this microgrid, each agent pursues an ED for its personal resources. In addition, each agent is capable of trading electricity with other agents through a local energy market. In this paper, an energy market operating in the presence of the grid is introduced. The proposed market is mainly developed for an experimental community microgrid at the Missouri University of Science and Technology, Rolla, MO, USA, and can be applied to other distribution level microgrids. To develop the algorithm, first, the microgrid is modeled and a dynamic ED algorithm for each agent is developed. Afterwards, an algorithm for handling the market is introduced. Lastly, simulation results are provided to demonstrate the proposed community market, and show the effectiveness of the market in reducing the operation costs of passive and active agents.

Adaptive load management (ALM) in electric power systems

In this work, we propose a multi-layered adaptive load management (ALM) system capable of integrating large-scale demand response in electric power systems efficiently and reliably. Electric power systems can be seen as a composition of multiple subsystems: power producers, load aggregators/utilities, end-users, etc. Focusing on the demand side of the system, we decompose the whole power system into three layers: the primary layer at the lowest level consisting of end-users of electric energy, the secondary layer or load aggregators that aggregate these endusers and provide service to them, and the tertiary level or the system/market operator at the highest level that incorporates and optimizes the objectives of the system as a whole. We pose the ALM problem as the problem of decomposing a complex network system using Lagrange decomposition techniques. Given the recent changes in electric energy systems, we propose that including more information from demand side helps improve the system-wide optimization. This is done by signaling a demand function, i.e. optimal energy use as a function of electricity price in place of a single point of a Lagrange multiplier, from the end-users at the primary layer to the higher layers. We provide a formulation of the systems decomposition problems of this multi-layered multi-directional decision making and information exchange. Our novel contribution will be showing that exchanging sensitivities of Lagrange coefficients instead of point-wise values of price data is essential for having a converging interactive information exchange within at the rate needed in physical power systems without storage. We identify open questions and plan for our future work.

Managing Contingencies in Smart Grids via the Internet of Things

This paper proposes a framework for contingency management using smart loads, which are realized through the emerging paradigm of the Internet of things. The framework involves the system operator, the load serving entities (LSEs), and the end-users with smart home management systems that automatically control adjustable loads. The system operator uses an efficient linear equation solver to quickly calculate the load curtailment needed at each bus to relieve congested lines after a contingency. Given this curtailment request, an LSE calculates a power allowance for each of its end-use customers to maximize the aggregate user utility. This large-scale NP-hard problem is approximated to a convex optimization for efficient computation. A smart home management system determines the appliances allowed to be used in order to maximize the user’s utility within the power allowance given by the LSE. Since the user’s utility depends on the near-future usage of the appliances, the framework provides the Welch-based reactive appliance prediction (WRAP) algorithm to predict the user behavior and maximize utility. The proposed framework is validated using the New England 39-bus test system. The results show that power system components at risk can be quickly alleviated by adjusting a large number of small smart loads. Additionally, WRAP accurately predicts the users’ future behavior, minimizing the impact on the aggregate users’ utility.

The effect of demand response on distribution system operation

Demand response is an important resource that can significantly increase the efficiency of the future power systems. It is a key component of smart grids that can bring a lot of benefits to power system operators and customers. This work assesses potential impacts of demand response on some major attributes of the distribution system such as the network losses, voltage profiles, and maximum power flow through the lines. We considered detailed and practical models of individual residential loads for flexible loads in the system. The flexible load models are various household appliances with different acceptable delay times (ADTs) within which their consumption can be shifted from the normal schedules. With these models, demand response was applied to the 33-bus IEEE test system. In this system, each bus served a specific number of aggregated individual residential loads. The obtained results have shown the great effects of demand response on distribution system attributes.