Multi-Objective Dynamic Economic Dispatch with Demand Side Management of Residential Loads and Electric Vehicles

In this paper, a multi-objective optimization method based on the normal boundary intersection is proposed to solve the dynamic economic dispatch with demand side management of individual residential loads and electric vehicles. The proposed approach specifically addresses consumer comfort through acceptable appliance deferral times and electric vehicle charging requirements. The multi-objectives of minimizing generation costs, emissions, and energy loss in the system are balanced in a Pareto front approach in which a fuzzy decision making method has been implemented to find the best compromise solution based on desired system operating conditions. The normal boundary intersection method is described and validated

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

Adaptive Load Management: Multi-Layered And Multi-Temporal Optimization Of The Demand Side In Electric Energy Systems

<p>Well-designed demand response is expected to play a vital role in operating<br>power systems by reducing economic and environmental costs. However,<br>the current system is operated without much information on the benefits of<br>end-users, especially the small ones, who use electricity. This thesis proposes a<br>framework of operating power systems with demand models including the diversity<br>of end-users’ benefits, namely adaptive load management (ALM). Since<br>there are a large number of end-users having different preferences and conditions<br>in energy consumption, the information on the end-users’ benefits needs<br>to be aggregated at the system level. This leads us to model the system in<br>a multi-layered way, including end-users, load serving entities, and a system<br>operator. On the other hand, the information of the end-users’ benefits can be<br>uncertain even to the end-users themselves ahead of time. This information is<br>discovered incrementally as the actual consumption approaches and occurs. For<br>this reason ALM requires a multi-temporal model of a system operation and<br>end-users’ benefits within. Due to the different levels of uncertainty along the<br>decision-making time horizons, the risks from the uncertainty of information<br>on both the system and the end-users need to be managed. The methodology<br>of ALM is based on Lagrange dual decomposition that utilizes interactive communication<br>between the system, load serving entities, and end-users. We show<br>that under certain conditions, a power system with a large number of end-users<br>can balance at its optimum efficiently over the horizon of a day ahead of operation<br>to near real time. Numerical examples include designing ALM for the<br>right types of loads over different time horizons, and balancing a system with a large number of different loads on a congested network. We conclude that<br>with the right information exchange by each entity in the system over different<br>time horizons, a power system can reach its optimum including a variety of<br>end-users’ preferences and their values of consuming electricity.</p

Aggregating Levelized Cost Functions in Microgrids for Transmission Grid Operation

While much research effort has been devoted to operation of microgrids, how the economic benefits of microgrids can be integrated into the transmission-level main grids remains as an open question. Motivated by this observation, we position the microgrid operation problem that is compatible with the main grid operation problem. This paper first presents an economic structure where multiple microgrids can be aggregated into the transmission-level main grids through load serving entities. Assuming that a microgrid energy manager (MEM) can bid into the market through a load serving entity, we solve the optimal scheduling of various distributed energy resources (DERs) in a microgrid. We construct detailed cost functions for DERs taking into account the costs of aging and fuel. The resulting cost functions can be non-increasing in terms of power, which is significantly different from cost functions of traditional bulk power producers, and can hinder MEMs from participating in the market. However, we show that as a result of the optimal scheduling of DERs, a MEM can obtain a single linear bid, which is the marginal cost of the microgrid system for each time step. This is compatible with the market structure at the main grid level. We also show the impact of including levelized costs of DERs compared to a case assuming zero short-Term costs

Integration of Sustainable Manufacturing Systems into Smart Grids with High Penetration of Renewable Energy Resources

Due to global climate change, many energy sectors are witnessing various efforts to make their systems more efficient and less pollutant. Demand response has been considered one of such efforts in power system operations. Industrial consumers of electricity, including manufacturing plants, have been attractive targets for demand response due to their large energy consumption scales. The idea of integrating manufacturing plants as a critical demand response resource in the context of smart grids with high penetration of renewable resources has drawn attention from both academia and industry. However, adjusting electricity demand considering the consumer\u27s constraints and objectives is critical for a successful and sustainable demand response program. Therefore, in this paper, the feasibility of such an integration considering the plants\u27 constraints is analyzed. The impact of demand response on smart manufacturing plants in a power system with high penetration of renewable energy resources is studied through a numerical case study, simulated on the modified IEEE 39-bus test system. The results show that overgeneration expected to occur especially with high solar generation can be mitigated with a large number of smart manufacturing systems, scheduled with the price signals

Collaborazione di Ricerca

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 contingencies. Given this curtailment request, an LSE calculates a power allowance for each of its end-use customers. We approximate this large-scale, NP-hard, problem 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 users’ utility depends on the nearfuture utilization of the appliances, we propose 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. We 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 their utility

Dynamic Economic Dispatch with Demand Side Management of Individual Residential Loads

Minimizing generation costs is one of the main purposes for system operators, since it decreases the customer\u27s energy costs and consequently increases the social welfare. Economic dispatch (ED) can be considered as a useful tool to determine the optimal generation cost. Extending the ED over multiple time steps is called the dynamic economic dispatch (DED). Demand side management (DSM) is a key component of smart grids that can have a lot of benefits to power system operators and customers. This paper aims to assess DSM potential impacts on electricity generation cost by considering the detailed and practical model of individual residential loads. For flexible loads, we assume various household appliances with different acceptable delay times (ADTs) within which their consumption can be shifted from the normal schedules. This problem was simulated on the 15-generators test systems. The results show that the first few MWs of shifting in demand bring out the largest decrease in generation costs

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

Multi-Objective Dynamic Economic Dispatch with Demand Side Management of Residential Loads and Electric Vehicles

In this paper, a multi-objective optimization method based on the normal boundary intersection is proposed to solve the dynamic economic dispatch with demand side management of individual residential loads and electric vehicles. The proposed approach specifically addresses consumer comfort through acceptable appliance deferral times and electric vehicle charging requirements. The multi-objectives of minimizing generation costs, emissions, and energy loss in the system are balanced in a Pareto front approach in which a fuzzy decision making method has been implemented to find the best compromise solution based on desired system operating conditions. The normal boundary intersection method is described and validated

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