The problem of distributing gas through a network of pipelines is formulated as a cost minimization subject to nonlinear flow-pressure relations, material balances, and pressure bounds. The solution method is based on piecewise linear approximations of the nonlinear flow-pressure relations. The approximated problem is solved by an extension of the Simplex method. The solution method is tested on real-world data and compared with alternative solution methods.

The gas transmission problem solved by an extension of the simplex algorithm

Achieving high renewable energy penetrated power systems requires considerable operational flexibility to hedge the variability and uncertainty of variable renewable energy (VRE) generation. Compared with VRE sources, concentrating solar power (CSP) is an emerging controllable renewable generation technique that utilizes solar thermal power to generate electricity. The operational dispatchability of CSP would contribute to the power system transition toward high renewable penetration. In this paper, we explore how the generation portfolio will change toward high renewable energy penetrations, how much cost is involved, and what role CSP will play in realizing a high renewable energy penetrated power system. This study relies on a stochastic two-stage generation and transmission expansion planning model with CSP plants. The model captures the uncertainty and variability of renewable generation and the flexibility limits of thermal plants. With the target of achieving a renewable-dominated minimum-cost system with an expected renewable energy penetration level, the investments of both generation and transmission facilities are optimized. A case study on IEEE test systems with renewable technology cost data in 2050 is performed to analyze the value of CSP toward high renewable energy penetrated power systems.

The Role of Concentrating Solar Power Toward High Renewable Energy Penetrated Power Systems

Real Options in Operations Research: A Review

In addition to energy, a concentrating solar power (CSP) plant with thermal energy storage (TES) could also provide ancillary service (AS) in the reserve and regulation markets. On one hand, providing AS contributes to the flexibility of the power systems and increases the revenue of CSP plants. On the other hand, the flexibility of CSP plants to accommodate solar energy, which is of great uncertainty, might be significantly weakened by an inappropriate offering strategy, e.g., offering excessive AS. Insufficient flexibility might cause massive solar energy curtailment and reduce the potential revenue. This paper develops a general model framework on the optimal offering strategy for CSP plants in joint day-ahead energy, reserve and regulation markets, which is robust for solar energy uncertainty and stochastic for market price uncertainty. On this basis, given the optimal day-ahead offering strategy, the offering curves to provide incremental AS capacities in the supplemental AS markets are further derived considering the opportunity cost. A new index, the maximum acceptable curtailment rate, is introduced to formulate the tradeoff of CSP plants between supplying AS to the system and reserving the flexibility for solar energy accommodation. The case study results demonstrate the validity of the proposed model.

Optimal Offering Strategy for Concentrating Solar Power Plants in Joint Energy, Reserve and Regulation Markets

With the continuous development of smart grid and further restructuring of power industry, modern power systems have been transformed to complex cyber-physical systems characterized with high renewable energy penetrations, distributed facilities, advanced metering, and communication technologies, as well as ever-increasing customer awareness. These new development trends pose significant challenges for electricity retailers and call for innovative decision-making methods. To help researchers and engineers have a better overview of the state-of-the-art on electricity retail decision-making schemes, this paper aims to survey the latest progress on this subject. Some critical and open issues in this field are also discussed.

Decision-Making for Electricity Retailers: A Brief Survey

Many countries and governments consider smart cities a solution to global warming, population growth, and resource depletion. Numerous challenges arise while creating a smart city. Digital twins, along with the Internet of Things, fifth-generation wireless systems, blockchain, collaborative computing, simulation, and artificial intelligence technologies, offer great potential in the transformation of the current urban governance paradigm toward smart cities. In this paper, the concept of a digital twin city (DTC) is proposed. The characteristics, key technologies, and application scenarios of a DTC are elaborated upon. Further, we discuss the theories, research directions, and framework regarding DTCs.

A systematic review of a digital twin city: A new pattern of urban governance toward smart cities

The uncertainty of wind power generation brings problems in power system operation, such as requiring more reserves and possible frequency issues. In this paper, we propose an idea of combining concentrating solar power (CSP) plants with wind farms to reduce the overall uncertainty in the joint power output. Taking advantage of the dispatchability of CSP, the uncertainty of joint power generation is expected to decrease. Based on the operational model of CSP plants with thermal storage system, we search for the narrowest but robust bounds of the joint power output with a given uncertainty of the wind power output and solar power availability, and within operational constraints of CSP plants. The problem is formulated as an adaptive robust optimization (RO) problem, containing mixed-integer variables at the second stage. We introduce an algorithm that combines a nested column-and-constraint generation (C-CG) method and an outer approximation (OA) method to solve the problem. The case studies show that robust intervals for the joint power output can be obtained, and the obtained intervals can be significantly narrower than the original intervals of wind power.

Reducing Generation Uncertainty by Integrating CSP With Wind Power: An Adaptive Robust Optimization-Based Analysis

We consider a utility who seeks to coordinate the energy consumption of multiple demand-side flexible resource aggregators. For the purpose of privacy protection, the utility has no access to the detailed information of loads of resource aggregators. Instead, we assume that the utility can directly observe each aggregator’s aggregate energy consumption outcomes. Furthermore, the utility can leverage resource aggregator energy consumption via time-varying electricity price profiles. Based on inverse optimization technique, we propose an estimation method for the utility to infer the energy requirement information of aggregators. Subsequently, we design a data-driven pricing scheme to help the utility achieve system-level control objectives (e.g., minimizing peak demand) by combining hybrid particle swarm optimizer with mutation algorithm and an iterative algorithm. Case studies have demonstrated the effectiveness of the proposed approach against two benchmark pricing strategies—a flat-rate scheme and a time-of-use scheme.

Data-Driven Pricing Strategy for Demand-Side Resource Aggregators

Several design guidelines and flexibility indices have been developed in the literature to inform the design of flexible production networks. In this paper, we propose additional flexibility design guidelines for unbalanced networks, where the numbers of plants and products are not equal, by refining the well-known Chaining Guidelines. We study symmetric networks, where all plants have the same capacity and product demands are independent and identically distributed, and focus mainly on the case where each product is built at two plants. We also briefly discuss cases where (1) each product is built at three plants and (2) some products are built at only one plant. An extensive computational study suggests that our refinements work very well for finding flexible configurations with minimum shortfall in unbalanced networks.

Tianhu Deng

Papers

A systematic review of a digital twin city: A new pattern of urban governance toward smart cities

Reducing Generation Uncertainty by Integrating CSP With Wind Power: An Adaptive Robust Optimization-Based Analysis

Data-Driven Pricing Strategy for Demand-Side Resource Aggregators

Process Flexibility Design in Unbalanced Networks

Capacity allocation under downstream competition and bargaining