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The growing adoption of renewable energy-based industrial microgrids challenges grid frequency stability. It highlights the need for microgrids to participate in Frequency Regulation (FR) service while preserving their economic benefits. Current policies, which prioritize FR participation based on revenue incentives, result in a suboptimal balance between economic and FR objectives. To address this issue, this study introduces a co-optimization approach and underscores its role in trade-off exploration. An actual case study validates the approach and reveals the conflict between these two objectives. The results of co-optimization reveal the impacts of Renewable Energy Sources (RES) and Energy Storage Systems (ESS) through Pareto front analysis. While RES can significantly reduce the power cost of EIIMs by up to 96.1%, it has a negative impact on frequency stability. This negative impact, measured through an energy-based method, results in deviations from the grid's preferred frequency regulation power by up to 154.0%. Meanwhile, the positive impact of ESS on improving the trade-off situation is demonstrated through the "Least Misery" principle. Compared to having no ESS, incorporating a 100 kW·h ESS achieves win-win outcomes, enhancing minimal dissatisfaction by 10.16%. Furthermore, a comparative analysis with the current policy approach showcases the risks of obtaining suboptimal solutions under existing policies. Some radical policy settings result in a dissatisfaction rate larger than 100%. This study elucidates the role of co-optimization by advancing comprehension of the impacts of RES and ESS while providing a method to achieve the appropriate trade-off. • Co-optimization balances economic and frequency regulation performance. • The proposed two-stage framework delineates the Pareto front efficiently. • Renewable energy reduces cost by up to 96.1% but affects stability by up to 154.0%. • Energy storage enhances minimal dissatisfaction by up to 10.16%. • Some radical revenue settings result in a dissatisfaction rate larger than 100%. [ABSTRACT FROM AUTHOR]

The power and transportation systems are urban interdependent critical infrastructures (CIs). During the post-disaster restoration process, transportation mobility and power restoration process are interdependent, and their functionalities significantly affect other well-beings of other urban CIs. Therefore, to enhance the resilience of urban CIs, successful recovery strategies should promote CI function cooperatively and synergistically to distribute goods and services efficiently. This paper develops an integrative framework that addresses the challenges of enhancing the recovery efficiency of urban power and transportation systems in short-term recovery period. Specifically, the post-storm recovery process is considered as a scheduling problem under the constraints representing crew dispatch, equipment and fuel limit. We propose a new framework for co-optimizing the recovery scheduling of power and transportation systems, respecting precedency requirement and network constraints. The advantages and benefits of co-optimized recovery scheduling are validated in a testing system.

Abstract As a significant clean energy source, natural gas plays an important role in modern energy context. The growing utilization of natural gas brings uncertainties into the power system, which requires an integrated way to plan natural gas and power systems. In this paper, the co-planning process is formulated as a mixed integer nonlinear programming problem to address emerging challenges, such as system reliability evaluation, market time line mismatch, market uncertainties, demand response effect, etc. An innovative expansion co-planning (ECP) framework is established in this paper to find the best augmentation plan which comes with the minimum cost. Specifically, to cope with uncertainties in market share, decision analysis is introduced. Meanwhile, the energy conversion efficiency between gas and electricity in the coupled load center is considered in the ECP constraints. Comprehensive case studies are applied to validate the performance of proposed approach.

Establishing clean and efficient building energy systems (BES) is an efficient path to promote the low-carbon energy transition to achieve the goal of carbon peak and carbon neutrality. To fully tap the energy saving potential of buildings, a co-optimization method combined active and passive energy-saving technologies is proposed for BES. The structures and characteristics of active and passive energy-saving means are modeled and analyzed. On this basis, a double-layer co-optimization model is built to optimize the planning and operation of BES separately. Furthermore, a carbon tax is introduced to establish the connection between active and passive means, and actual uncertainties of source-load are considered through the probabilistic scenario generation and interval linear programming approach. Case studies on the BES in Xiong'an New Area, China show that the proposed co-optimization method saves about 2.2%–3.4% costs than considering only the active energy-saving. Analysis of forecast errors for different scenarios reveals planning options and detailed costs under different levels of uncertainty. • Active and passive energy-saving techniques are co-optimized for buildings. • The passive energy-saving means are modeled and simulated in the design stage. • Envelopes and renewable energy penetration are promoted with the carbon tax. • Scenario generation and interval linear programming are proposed for uncertainty. [ABSTRACT FROM AUTHOR]

With integration of more and more renewable energy resources, it is becoming increasingly difficult to maintain adequate primary frequency control (PFC) capability for a future power grid, especially under low system inertia conditions. Load resources (LRs) equipped with under-frequency relays can participate in PFC supplementing to the governor responses from synchronous units. In this paper, we propose an energy, inertia and frequency response reserve (FRR) co-optimization formulation in the day-ahead market where both primary frequency reserve (PFR) from synchronous generators and fast frequency response reserve (FFR) from LRs are procured in a cooperative way to meet the desired FRR need tied to the system inertia condition. Since FFR is more effective than PFR in arresting the frequency decline, the proposed approach will yield different marginal prices for FFR and its PFR counterpart to award the speed of response. As the formulation proposed involves bilinear terms in optimization problem, a linear reformulation techniques with big M is proposed to transform the problem into a mixed integer linear programming which can be solved by the commercial solver CPLEX. The case study shows the effectiveness of the proposed approach and the correctness of the quantities and prices of the cleared reserve. [ABSTRACT FROM AUTHOR]

Renewables are increasingly penetrating power systems and impacting electricity markets supported by the stricter energy and environmental policies. To handle the variability and uncertainty of renewable generation and to provide transparent economic incentives for resources to provide flexible services, a bid-based flexible ramping products market is proposed in the CAISO and MISO markets. To investigate the impact of these new products on the market equilibrium, a multi-period Nash-Cournot equilibrium model, formulated as a bi-level optimization problem, was proposed, and the Guass-Seidel iterative method was used to obtain the equilibrium. Moreover, a general framework of co-optimization of energy and flexible ramping products was established, and different types of generators, including thermal units, hydro units, renewable units and energy storage systems, are simultaneously considered to reflect their strategic interactions. Two cases with dominant solar power and wind power, respectively, have been implemented and are compared to demonstrate the impact of flexible ramping products on market prices, unit commitment and renewable integration. Additional energy storage systems are also included in the above case for further analysis. Simulation results show that when introducing the new products: the energy prices will increase slightly under normal conditions, more highly variable renewables can be integrated, the unit commitment will be changed, and more generators should be on line to provide flexible services, etc. [ABSTRACT FROM AUTHOR]

Demand-Response (DR) has emerged as a valuable resource option for balancing electricity supply and demand. However, traditional power system models have neglected to include DR within long-term expansion problems. We can summarize our scientific contributions in the following aspects: (i) design of a new integrated co-optimization planning model for supply and demand coordination; (ii) assessment of the technical and economic impact of DR for systems with a high share of Renewable Energy Sources (RES) and (iii) proposal of the 'opportunity cost' concept for computing the price of not meeting the demand. Findings of this research support the hypothesis that DR scenarios reveal a high potential for delaying future investments in power capacity compared to scenario BAU (Business as Usual). However, it was found a limited potential of DR to integrate additional renewable plants. This research has provided further evidence concerning the potential of DR to decrease the levels of CO 2 emissions that is strictly related to the reduced need for fossil fuel thermal power plants. Given the high RES share, uncertainties related to future weather conditions must be however highlighted. This study concludes on the importance of DR for power systems planning and lays the groundwork for future research. • Design of a new integrated co-optimization planning model for supply and demand coordination. • Technical and economic DR potentials are assessed for a renewable-based energy system. • DR shows a high potential for reducing the need for additional thermal capacity. • Uncertainties related to future weather conditions might affect the cost-effective DR potential. • The spot prices might significantly affect the optimal scenarios with DR. [ABSTRACT FROM AUTHOR]

Increasing energy efficiency in water distribution systems is one of the most important aspects of a sustainable water infrastructure. Additionally, providing this energy from renewable sources is an essential step towards a cleaner production in energy infrastructures. Therefore in this paper, an optimization model is developed to minimize the energy consumption of a water-energy microgrid system. A day-ahead economic dispatch model is developed to minimize the daily cost of energy in the water-energy microgrid. The energy consumption of water system is minimized using tank's and pump's scheduling and operation, hydraulic factors, and daily demand. Particularly, the electricity consumption of the pump is minimized by adjusting its head gain and flow rate via a variable speed. The energy unit is composed of an aggregated conventional power generation, solar photovoltaic, wind generation, and battery energy storage system. To offer a global optimum for the proposed non-linear programming formulation, bivariate piecewise linear approximation is used to linearize the pump's power consumption and gain, and conservation of energy-mass. In addition, univariate piecewise linear approximation is used to linearize the energy consumption function of conventional power generation systems. The optimization results of the mixed integer linear programming and mixed integer non-linear programming formulations for the studied water-energy microgrid system are compared and discussed. These models are developed to concurrently minimize the electricity consumption of a micro water-energy distribution network in two scenarios of (1) standalone water distribution system operation and (2) an integrated islanded micro water-energy system with electrical loads. • Energy consumption of standalone micro water system is formulated. • Co-optimization of energy and water demand in water-energy microgrid is studied. • Via piecewise linearization, nonlinear functions presented as linear approximates. • Unit commitment problem was formulated for distributed generation units. • A microgrid with battery storage and renewable sources is considered. [ABSTRACT FROM AUTHOR]

• Energy consumption of a standalone micro water system is optimized. • Energy consumption of an islanded a micro water-energy system is optimized. • Random behavior of renewable energy sources is considered for the energy system. • Battery energy storage charging/discharging is used in the micro energy system. • Variable speed and quadratic functions for the head of the pumps are considered. Water and energy systems are interdependent. However, at national and international levels, energy and water systems have been designed individually. To optimize the use of energy resources and have a more sustainable energy processes, a new formulation is proposed in this paper to optimize the energy consumption of water-energy systems at a community scale. More specifically, single-objective, bi-level, and co-optimization models are developed to minimize the energy consumption of a micro water distribution network concerning three scenarios: (1) standalone operation; (2) integrated with a grid-connected micro energy system with no storage unit; and (3) integrated with an off-grid micro energy system with storage units. In all conditions, a mixed integer nonlinear programming formulation is used to solve the optimization problems. Pump operations with varying statuses, flow rates, and speeds are contemplated to formulate the energy consumption of the micro water, considering a quadratic function for the pump's energy head changing with flow rate. The micro water network is designed based on a diurnal pattern of water demand for a network including 1 reservoir, 1 water tank, 6 nodes, and 2 pumps. The micro energy system includes a microgrid with a combined heat and power plant (CHP), diesel (DS) generator, natural gas (NG) generator, renewable sources (solar and wind), and energy storage units. Several case studies are carried out to compare the performance of developed optimization models. [ABSTRACT FROM AUTHOR]

This paper proposes a system model for optimal dispatch of the energy and reserve capacity considering uncertain load demand and unsteady power generation. This implicates uncertainty in managing the power demand along with the consideration of utility, user and environmental objectives. The model takes into consideration a day-ahead electricity market that involves the varying power demand bids and generates a required amount of energy in addition with reserve capacity. The lost opportunity cost is also considered and incorporated within the context of expected load not served. Then, the effects of combined and separate dispatching the energy and reserve are investigated. The nonlinear cost curves have been addressed by optimizing the objective function using robust optimization technique. Finally, various cases in accordance with underlying parameters have been considered in order to conduct and evaluate numerical results. Simulation results show the effectiveness of proposed scheduling model in terms of reduced cost and system stability. [ABSTRACT FROM AUTHOR]