TimeGAN based distributionally robust optimization for biomass-photovoltaic-hydrogen scheduling under source-load-market uncertainties

Seismic events can severely damage both electricity and natural gas systems, causing devastating consequences. Ensuring the secure and reliable operation of the integrated energy system (IES) is of high importance to avoid potential damage to the infrastructure and reduce economic losses. This article proposes a new optimal two-stage optimization to enhance the reliability of IES planning and operation against seismic attacks. In the first stage, hardening investment on the IES is conducted, featuring preventive measures for seismic attacks. The second stage minimizes the expected operation cost of emergency response. The random seismic attack is modeled as uncertainty, which is realized after the first stage. An explicit damage assessment model is developed to define the budget set of the uncertain seismic activity. Based on the survivability of transmission lines and gas pipelines of IES, an optimal system investment plan is developed. The problem is formulated as a two-stage distributionally robust optimization (DRO) model, which is tested on an integrated IEEE 30-bus system and 20-node gas network. Case studies demonstrate that the two-stage DRO outperforms robust optimization and a single-stage optimization model in terms of minimizing the investment cost and expected economic loss. This article can help system operators to make economical hardening and operation strategies to improve the reliability of IES under seismic attacks, thus managing a more robust and secure energy system.

Distributionally robust optimization for collaborative emergency response network design

This paper assesses quantitative estimates based on economic modelling studies of the economic and environmental benefits from different forms of international co-ordination on carbon pricing. Forms of international co-ordination include: harmonising carbon prices (e.g. through linking carbon markets), extending the coverage of pricing schemes, phasing out fossil fuel subsidies, developing international sectoral agreements, and establishing co-ordination mechanisms to mitigate carbon leakage. All forms of international co-operation on carbon pricing can deliver benefits, both economic (e.g. lower mitigation costs) and/or environmental (e.g. reducing GHG emissions and carbon leakage). Benefits tend to be higher with broader participation of countries, broader coverage of emissions and sectors and more ambitious policy goals. Most, but not all, countries gain economic benefits from international co-operation, and these benefits vary significantly across countries and regions. Complementary measures outside co-operation on carbon pricing (e.g. technology transfers) could ensure that co-operation provides economic benefits for all countries.

Distributionally robust optimization (DRO) has been introduced for solving stochastic programs in which the distribution of the random variables is unknown and must be estimated by samples from that distribution. A key element of DRO is the construction of the ambiguity set, which is a set of distributions that contains the true distribution with a high probability. Assuming that the true distribution has a probability density function, we propose a class of ambiguity sets based on confidence bands of the true density function. As examples, we consider the shape-restricted confidence bands and the confidence bands constructed with a kernel density estimation technique. The former allows us to incorporate the prior knowledge of the shape of the underlying density function (e.g., unimodality and monotonicity), and the latter enables us to handle multidimensional cases. Furthermore, we establish the convergence of the optimal value of DRO to that of the underlying stochastic program as the sample size increases. The DRO with our ambiguity set involves functional decision variables and infinitely many constraints. To address this challenge, we apply duality theory to reformulate the DRO to a finite-dimensional stochastic program, which is amenable to a stochastic subgradient scheme as a solution method.

Distributionally robust optimization for dispatching the integrated operation of rural multi-energy system and waste disposal facilities under uncertainties

Optimal dispatch of a novel integrated energy system combined with multi-output organic Rankine cycle and hybrid energy storage

Quay cranes are among the most important resources in port terminals, and their efficient use is crucial for port terminals to remain competitive in the market. The problem of determining the sequence of tasks performed by the quay cranes that minimizes the turnaround time of a vessel is an NP‐hard problem known as the quay crane scheduling problem (QCSP).In this paper, we consider the unidirectional QCSP under uncertain processing times, where the cranes are only allowed to move in a specific direction. We start by presenting the first distributionally robust optimization (DRO) model for this problem and an exact decomposition algorithm to solve it. The proposed DRO model makes it possible to derive a stochastic programming model and a robust optimization model for the unidirectional QCSP by an appropriate choice of the risk‐averse parameter of the model. Through extensive numerical results, we compare these three approaches—stochastic programming, robust optimization, and DRO—to investigate whether significant differences among the solutions obtained exist. Finally, we propose a new method for helping practitioners to determine a representative set of different DRO solutions.

Distributionally robust facility location with uncertain facility capacity and customer demand

A negative-carbon planning method for agricultural rural industrial park integrated energy system considering biomass energy and modern agricultural facilities

Reliable Facility Location Under Event-Correlated Uncertainty

In order to improve the level of new energy consumption in the system and utilize the clean and efficient characteristics of hydrogen energy, an integrated energy system (IES) scheduling model considering refined utilization of hydrogen energy and generalized energy storage is proposed. Firstly, the two-stage hydrogen energy utilization model of power-to-gas (P2G) is finely modeled, and the waste heat of the P2G methanation reaction is innovatively coupled with the Kalina cycle to improve the thermoelectric decoupling capability of the combined heat and power (CHP) unit. Secondly, integrated demand response, electric vehicles, and hydrogen-containing multi-source energy storage equipment are used as generalized energy storage resources to cut peaks and fill valleys. Then, on the basis of considering the ladder-type carbon trading mechanism, the IES conventional operation model is constructed with the minimum operating cost of the system as the objective function. Furthermore, considering the source-load uncertainty of IES operation, a multi-energy complementary optimal scheduling model of hydrogen-containing IES based on conditional value-at-risk was established. Through simulation analysis, it can be seen that the proposed model takes into account both economic and environmental benefits and improves the system’s ability to “peak cutting and valley filling” and measure risk levels.

This paper proposes an optimization method based on Multi-Objective Particle Swarm Optimization (MOPSO) for addressing the operational optimization challenges of Combined Cooling, Heating, and Power (CCHP) integrated energy systems. CCHP systems enhance energy efficiency and reduce environmental pollution, thus possessing significant economic and environmental benefits. Despite existing research progress, current methodologies still inadequately address the simultaneous optimization of economic and environmental aspects. The key contributions of this research include constructing an operational optimization model for the CCHP system, improving the MOPSO algorithm, specifically in terms of inertia weight, learning factors, and individual optimal values, and applying these improvements to solve the model. The theoretical foundations of the CCHP system, multi-objective optimization problems, principles of Particle Swarm Optimization (PSO), and the characteristics and advantages of MOPSO are discussed comprehensively. The optimization model targets minimizing economic costs and optimizing environmental performance, clearly defining decision variables and constraints, and rigorously evaluating MOPSO algorithm applicability. A detailed procedure for constructing and solving the optimization model is provided. A case study is conducted by establishing background information, setting system parameters, configuring MOPSO algorithm parameters, and performing the optimization. Results are thoroughly analyzed, comparing the method's effectiveness against other optimization methods to validate its superiority. The study concludes that this approach effectively optimizes CCHP operations, providing a reference for coordinated planning in integrated energy systems, and discusses future research directions.

The Case against Carbon Prices

Low-carbon economic scheduling for AIES considering flexible load coordination and multiple uncertainties

Cost-benefit analysis of integrated energy system planning considering demand response