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.

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.

Over the past few decades, the concept of resilience has emerged as an important consideration in the planning and management of water infrastructure systems. Accordingly, various resilience measures have been developed for the quantitative evaluation and decision-making of systems. There are, however, numerous considerations and no clear choice of which measure, if any, provides the most appropriate representation of resilience for a given application. This study provides a critical review of quantitative approaches to measure the resilience of water infrastructure systems, with a focus on water resources and distribution systems. A compilation of 11 criteria evaluating 21 selected resilience measures addressing major features of resilience is developed using the Axiomatic Design process. Existing gaps of resilience measures are identified based on the review criteria. The results show that resilience measures have generally paid less attention to cascading damage to interrelated systems, rapid identification of failure, physical damage of system components, and time variation of resilience. Concluding the paper, improvements to resilience measures are recommended. The findings contribute to our understanding of gaps and provide information to help further improve resilience measures of water infrastructure 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.

This paper proposes a model for optimizing the energy flexibility of water distribution systems (WDSs) in day-ahead power systems operation. The water distribution system operators (W-DSOs) are considered as energy-conscious entities, who run the proposed WDS operation model to optimize the operation of pumps and tanks for minimizing the operation cost of local WDSs, given the expected water demand and electricity prices of the next day. A WDS energy flexibility model is proposed that is used by W-DSOs in order to calculate and offer the feasible flexible energy capacity to the power system operator. The proposed WDS operation model takes into account the hydraulic operating constraints of water networks, thus ensuring deliverability of the WDS energy flexibility. Further, a network-constrained unit commitment model is proposed that integrates and optimizes the energy flexibility of WDSs in the day-ahead operation of power systems. The proposed model is implemented on a 15-node WDS that is powered by a 6-bus power system and the results are presented to study the impacts of optimized WDS energy flexibility on power and water systems operation.

This paper presents a hierarchical demand response (DR) bidding framework in the day-ahead energy markets which integrates customer DR preferences and characteristics in the ISO's market clearing process. In the proposed framework, load aggregators submit aggregated DR offers to the ISO which would centrally optimize final decisions on aggregators' DR contributions in wholesale markets. The hourly load reduction strategies include load shifting and curtailment and the use of onsite generation and energy storage systems. The ISO applies mixed-integer linear programming (MILP) to the solution of the proposed DR model in the day-ahead market clearing problem. The proposed model is implemented using a 6-bus system and the IEEE-RTS, and several studies are conducted to demonstrate the merits of the proposed DR model.