Masood Parvania

Demand Response Scheduling by Stochastic SCUC

Considerable developments in the real-time telemetry of demand-side systems allow independent system operators (ISOs) to use reserves provided by demand response (DR) in ancillary service markets. Currently, many ISOs have designed programs to utilize the reserve provided by DR in electricity markets. This paper presents a stochastic model to schedule reserves provided by DR in the wholesale electricity markets. Demand-side reserve is supplied by demand response providers (DRPs), which have the responsibility of aggregating and managing customer responses. A mixed-integer representation of reserve provided by DRPs and its associated cost function are used in the proposed stochastic model. The proposed stochastic model is formulated as a two-stage stochastic mixed-integer programming (SMIP) problem. The first-stage involves network-constrained unit commitment in the base case and the second-stage investigates security assurance in system scenarios. The proposed model would schedule reserves provided by DRPs and determine commitment states of generating units and their scheduled energy and spinning reserves in the scheduling horizon. The proposed approach is applied to two test systems to illustrate the benefits of implementing demand-side reserve in electricity markets.

Optimal Demand Response Aggregation in Wholesale Electricity Markets

Advancements in smart grid technologies have made it possible to apply various options and strategies for the optimization of demand response (DR) in electricity markets. DR aggregation would accumulate potential DR schedules and constraints offered by small- and medium-sized customers for the participation in wholesale electricity markets. Despite various advantages offered by the hourly DR in electricity markets, practical market tools that can optimize the economic options available to DR aggregators and market participants are not readily attainable. In this context, this paper presents an optimization framework for the DR aggregation in wholesale electricity markets. The proposed study focuses on the modeling strategies for energy markets. In the proposed model, DR aggregators offer customers various contracts for load curtailment, load shifting, utilization of onsite generation, and energy storage systems as possible strategies for hourly load reductions. The aggregation of DR contracts is considered in the proposed price-based self-scheduling optimization model to determine optimal DR schedules for participants in day-ahead energy markets. The proposed model is examined on a sample DR aggregator and the numerical results are discussed in the paper.

Integrating Load Reduction Into Wholesale Energy Market With Application to Wind Power Integration

Renewable energy resources, notably wind power, are expected to provide considerable portion of the world energy requirements in the near future. Many system operators around the world are challenged by the problems associated with integrating these intermittent resources into the grid. As one of the potential solutions, demand response (DR) is expected to play a major role for mitigating integration issues of intermittent renewable energy resources. In this context, this paper proposes a DR program which helps to integrate wind power by reshaping the load of the system. The DR program provides a framework to procure load reduction from DR resources in the wholesale energy market. The participants in the program submit their offer packages to provide load reduction in the day-ahead energy market. A day-ahead network-constrained market clearing formulation is also proposed which considers the load reduction provided by the DR program participants as an energy market commodity. The proposed method, which is in the mixed-integer linear programming format, determines commitment state of generating units, schedules the energy and spinning reserve provided by generating units, and schedules the load reduction provided by the DR program participants. To reveal the features of the proposed method, several numerical studies are conducted on the IEEE-RTS. The results presented indicate that integrating load reduction in the energy market provides a powerful tool to selectively modify the system load to support wind power integration, while making significant economic and technical benefits for the system.

Optimized Sectionalizing Switch Placement Strategy in Distribution Systems

Automation is acknowledged by distribution utilities as a successful investment strategy to enhance reliability and operation efficiency. However, practical approaches that can handle the complex decision-making process faced by decision makers to justify the long-term financial effects of distribution automation have remained scarce. An automated and remote-controlled sectionalizing switch play a fundamental role in an automated distribution network. This paper introduces a new optimization approach for distribution automation in terms of automated and remotely controlled sectionalizing switch placement. Mixed-integer linear programming (MILP) is utilized to model the problem. The proposed model can be solved with large-scale commercial solvers in a computationally efficient manner. The proposed sectionalizing switch placement problem considers customer outage costs in conjunction with sectionalizing switch capital investment, installation, as well as annual operation and maintenance costs. The effectiveness of the proposed approach is tested on a reliability test system and a typical real size system. The presented results indicate the accuracy and efficiency of the proposed method.

A Two-Stage Framework for Power Transformer Asset Maintenance Management—Part I: Models and Formulations

Summary form only given. The emergence of smart grid technologies in terms of advanced communication infrastructure, embedded intelligence, diagnostics and monitoring capabilities offers new opportunities for improved transmission asset management strategies (TAMS). Accordingly, power system operators are currently looking for analytics that can make use of transmission asset condition monitors and data already available to make better-informed decisions. This two-part paper introduces a two-stage maintenance scheduler for power transmission assets. Part I begins with the motivation for TAMS and then continues with a two-stage maintenance management model that incorporates joint midterm and short-term maintenance. The first stage involves a midterm asset maintenance scheduler that explicitly considers the asset condition dynamics in terms of failure rate. The second stage introduces a short-term maintenance scheduler with N-1 reliability that schedules the output of the midterm maintenance scheduler in the short run. The midterm and short-term stages are completely decoupled schemes to make the problem computationally tractable. For the sake of exposition here, we focus on the maintenance of grid transformers. The proposed methodology is general, however, and can be extended to other network equipments as well. The characteristics of the proposed model and its benefits are investigated in Part II through several case studies.

A hybrid network IDS for protective digital relays in the power transmission grid

In this paper, we propose a novel use of network intrusion detection systems (NIDSs) tailored to detect attacks against networks that support hybrid controllers that implement power grid protection schemes. In our approach, we implement specification-based intrusion detection signatures based on the execution of the hybrid automata that specify the communication rules and physical limits that the system should obey. To validate our idea, we developed an experimental framework consisting of a simulation of the physical system and an emulation of the master controller, which serves as the digital relay that implements the protection mechanism. Our Hybrid Control NIDS (HC-NIDS) continuously monitors and analyzes the network traffic exchanged within the physical system. It identifies traffic that deviates from the expected communication pattern or physical limitations, which could place the system in an unsafe mode of operation. Our experimental analysis demonstrates that our approach is able to detect a diverse range of attack scenarios aimed at compromising the physical process by leveraging information about the physical part of the power system.

Hybrid Control Network Intrusion Detection Systems for Automated Power Distribution Systems

In this paper, we describe our novel use of network intrusion detection systems (NIDS) for protecting automated distribution systems (ADS) against certain types of cyber attacks in a new way. The novelty consists of using the hybrid control environment rules and model as the baseline for what is normal and what is an anomaly, tailoring the security policies to the physical operation of the system. NIDS sensors in our architecture continuously analyze traffic in the communication medium that comes from embedded controllers, checking if the data and commands exchanged conform to the expected structure of the controllers interactions, and evolution of the systems physical state. Considering its importance in future ADSs, we chose the fault location, isolation and service restoration (FLISR) process as our distribution automation case study for the NIDS deployment. To test our scheme, we emulated the FLISR process using real programmable logic controllers (PLCs) that interact with a simulated physical infrastructure. We used this test bed to examine the capability of our NIDS approach in several attack scenarios. The experimental analysis reveals that our approach is capable of detecting various attacks scenarios including the attacks initiated within the trusted perimeter of the automation network by attackers that have complete knowledge about the communication information exchanged.

Assessing impact of demand response in emission-constrained environments

Reducing the emissions produced by the electric power production may be one of the most challenging problems of the electricity sector in the coming future. As one of the potential solutions, demand response (DR) can play an important role to reduce emissions and costs associated with emission reduction activities. This paper aims to assess the short-term impacts of running a DR program on a power system constrained by emissions caps. The DR program is designed to procure operating reserve from demand-side participants. A day-ahead network-constrained market clearing model with emission cap constraints is used as the assessment tool, where the DR program participants along with generating units are considered as available resources to provide reserve for the system. A model is also presented for reserve provided by DRPs and its associated cost function. The proposed approach is applied to the IEEE-RTS to illustrate the impacts of the DR program.

Reliability-constrained unit commitment using stochastic mixed-integer programming

This paper proposes a stochastic mixed-integer programming (SMIP) model for the reliability-constrained unit commitment (RCUC) problem. The major objective of the paper is to examine both features of accuracy and efficiency of the proposed SMIP model of RCUC. The spinning reserve of generating units is considered as the only available reserve provision resource; however, the proposed formulation can be readily extended to comprise the other kind of reserve facilities. Expected load not served (ELNS) and loss of load probability (LOLP) are accommodated as the reliability constraints. Binding either or both reliability indices ensures the security of operation incorporating the stochastic nature of component outages. In this situation, the spinning reserve requirement is no longer considered explicitly. The Monte Carlo simulation method is used to generate scenarios for the proposed SMIP model. The scenario reduction method is also adopted to reduce computation burden of the proposed method. The IEEE reliability test system (RTS) is employed to numerically analyze the proposed model and the implementation issues are discussed. The simulations are conducted in the single- and multi-period bases and the performance of the model is investigated verses different reliability levels and various numbers of scenarios.

Unit Commitment With Continuous-Time Generation and Ramping Trajectory Models

There is increasing evidence of shortage of ramping resources in the real-time operation of power systems. To explain and remedy this problem systematically, in this paper we take a novel look at the way the day-ahead unit commitment (UC) problem represents the information about load, generation and ramping constraints. We specifically investigate the approximation error made in mapping of the original problem, that would decide the continuous-time generation and ramping trajectories of the committed generating units, onto the discrete-time problem that is solved in practice. We first show that current practice amounts to approximating the trajectories with linear splines. We then offer a different representation through cubic splines that provides physically feasible schedules and increases the accuracy of the continuous-time generation and ramping trajectories by capturing sub-hourly variations and ramping of load in the day-ahead power system operation. The corresponding day-ahead UC model is formulated as an instance of mixed-integer linear programming (MILP), with the same number of binary variables as the traditional UC formulation. Numerical simulation over real load data from the California ISO demonstrate that the proposed UC model reduces the total day-ahead and real-time operation cost, and the number of events of ramping scarcity in the real-time operations.