Efficient Power centric Resilience Maximization Model for Distribution System Using Adaptive Configuration

The resilience is the most dominant factor in any disaster condition which has been approached by various techniques in literature. Some of the methods consider only the power availability and grid states for the resilience maximization. However, they suffer to achieve higher performance in voltage distribution. To solve this issue, an efficient Power Centric Resilience Maximization Model (PCRMM) is presented in this paper. The model monitors the incoming voltage and generates adaptive configuration for the distribution system by considering number of grids, their states, emergency units, essential units, service sectors and so on. By considering all these factors, the method computes the resilience support for different micro grids according to the available voltage. Also, the method computes the functional support for the cycle and based on that dynamic configurations are generated. According to the configuration, the distribution system would trigger the voltage supply to various micro grids to feed voltage to the selected units. The proposed method improves the performance of power distribution with least voltage loss

Two-stage Robust-Stochastic Electricity Market Clearing Considering Mobile Energy Storage in Rail transportation

This paper proposes a two-stage robust-stochastic framework to evaluate the effect of the battery-based energy storage transport (BEST) system in a day-ahead market-clearing model. The model integrates the energy market-clearing process with a train routing problem, where a time-space network is used to describe the limitations of the rail transport network (RTN). Likewise, a price-sensitive shiftable (PSS) demand bidding approach is applied to increase the flexibility of the power grid operation and reduce carbon emissions in the system. The main objective of the proposed model is to determine the optimal hourly location, charge/discharge scheduling of the BEST system, power dispatch of thermal units, flexible loads scheduling as well as finding the locational marginal price (LMP) considering the daily carbon emission limit of thermal units. The proposed two-stage framework allows the market operator to differentiate between the risk level of all existing uncertainties and achieve a more flexible decision-making model. The operator can modify the conservatism degree of the market-clearing using a non-probabilistic method based on info-gap decision theory (IGDT), to reduce the effect of wind power fluctuations in real-time. In contrast, a risk-neutral-based stochastic technique is used to meet power demand uncertainty. The results of the proposed mixed-integer linear programming (MILP) problem, confirm the potential of BEST and PSS demand in decreasing the LMP, line congestion, carbon emission, and daily operation cost

A comprehensive review of demand side management in distributed grids based on real estate perspectives

A major challenge in renewable energy planning and integration with existing systems is the management of intermittence of the resources and customer demand uncertainties that are attributed to climates. In emerging distributed grids, state-of-the-art optimization techniques were used for cost and reliability objectives. In the existing literature, power dispatch and demand side management schemes were implemented for various techno-economic objectives. In renewable energy-based distributed grids, power dispatch is strategic to system operations. However, demand side management is preferred, as it allows more options for customer participation and active management of energy in buildings. Moreover, the demand side management can simply follow supplies. This paper investigates the implications of demand side management as it affects planning and operations in renewable energy-based distributed grids. Integration of demand side management in customer-oriented plans such as the time-of-use and real-time-pricing on residential and commercial demands is conceptualised to ensure effective customer participation which maintains the valued comforts. Moreover, the optimised tariff integrated demand side management implementations based on the utility-initiated demand response programmes are envisaged to offset conflicting objectives of the economy and customer comforts within residential and commercial demands and are also viewed as a step towards efficient management of energy in buildings

Optimization of Islanded Utility-Microgrids After Natural Disasters

Natural disasters can cause widespread disturbances/power outages within distribution networks and hinder a utility’s ability to provide uninterrupted power supply to the critical public buildings (e.g., hospitals, grocery stores, fire, police and gas stations) within the utility’s serviced region. Backup generators, which are typically relied on during power interruptions, have limited capacities and have been reported to experience failures during usage. Microgrids, defined as localized power grids that incorporate distributed generators (DGs) and energy storage systems (ESSs) to allow them to operate independent of the main grid (i.e., island mode), can help utilities provide disaster relief power supply to critical public buildings during such outages. This research investigates the optimization of utility-owned microgrids assumed to be operating in island mode and supplying power to a network of critical public buildings over the course of a week-long power outage. A deterministic and two-stage stochastic model (considering only DGs), as well as a multi-stage stochastic model (considering DGs and ESSs) are developed to optimize the investment economics, reliability and resilience of the microgrids. The models provides a holistic objective function that captures the investment, fixed operation and maintenance, power supply efficiency, reliability and resilience of the microgrid in terms of a minimized total cost to the utility. This is accomplished by optimizing the location, sizing, power supply assignment and total number of DGs and ESSs within a utility-owned microgrid. Hourly and weather (cloud coverage) uncertainty in daily DG power output and critical public building demand are considered. The final DG-plus-ESS multi-stage model provides an exhaustive solution framework, that analyzes the microgrid’s reliability across all possible weather (cloud coverage) scenarios (e.g., sunny, cloudy, overcast) of a week-long outage (3,279 total scenarios)