Rising energy expenses, the shift towards renewable sources, and grid congestion considerably affect the operations of container terminals. To tackle these challenges, it is necessary to implement energy-aware integrated operational planning which considers related uncertainties. This work proposes a two-stage stochastic mixed integer programming model to optimize container terminal operations planning and demand-responsive energy management. To this end, energy consumption is shifted whenever operationally possible and economically beneficial. We solve the proposed model by developing a dedicated progressive hedging algorithm. Operations considered in this model include vessel scheduling at berths, temperature control of refrigerated containers, and allocation of handling capacity of quay cranes, yard cranes, and automated guided vehicles to serve each vessel. Various scenarios for vessel arrival times and electricity prices are explored representing the uncertainty of energy demand and supply, respectively, based on a case study of the Altenwerder container terminal in Hamburg. Our results suggest potential cost savings of 5.9 per cent on average with a single energy price based on a long-term contract and 13.2 per cent when applying varying real-time electricity prices based on wholesale market rates. These findings underscore the substantial potential of demand response strategies for (electrified) container terminal operations.

Industrial greenhouse gas emissions, primarily carbon dioxide, constitute about one-third of global emissions, and 75% are caused by the generation of heat from fossil fuels. Therefore, a key decarbonisation strategy is electrifying heat generation using renewable sources and power-to-heat technologies. This study explores the impact of the energy price on the optimal choice and sizing of power-to-heat and storage technologies in existing energy-intensive industries with a variable heat demand. A mixed integer linear program is used to determine the technology portfolio and size of the equipment that leads to the lowest total annual cost of the utility system while ensuring that heat demand is always fulfilled. The results of a case study in the Netherlands show that adding power-to-heat and storage technologies to a fossil fuel-based combined heat and power plant is economically viable under all explored scenarios. The mean and the variance of electricity prices significantly influence the sizing of heat pumps, electric boilers, and thermal energy storage. High and stable electricity prices lead to larger heat pump capacities compared to scenarios with low and more variable electricity prices. Electric boilers are primarily sized based on the variance of electricity prices and the capacity of thermal energy storage, which plays a crucial role in managing electricity price fluctuations. The study emphasises the potential for cost-effective electrification and provides valuable insights for reducing industrial CO₂ emissions.

The electrification of utility systems in energy-intensive plants is a promising measure for decarbonising the chemical industry in the short term. However, with the increasing deployment of renewable energy sources, the variability of electricity prices will become a challenge for plants with continuous and constant energy demand. It is thus uncertain whether electrification can become financially viable. This work models the electrification of utility systems in combination with storage technologies for five chemical plants with existing fossil fuel-based utility generation and uses historical data as energy price scenarios. The results show that partial electrification is cost-effective when using electricity is cheaper than natural gas for more than 600 h. Regarding the portfolio of technologies, electric boilers are installed first, followed by thermal energy storage and batteries. Hydrogen is not cost-effective in any of the scenarios explored. This is independent of the type of plant, the available grid connection capacity, and the minimal load of existing fossil fuel-based utility generation. This work thus highlights the potential for electrifying industrial utility systems and the role that electric boilers and energy storage units can play in electrification.

Coordinating the interactions between increasingly interconnected energy sectors and carriers can lead to an efficient integration of variable renewable energy (VRE) resources, and a more cost-efficient energy transition. This paper proposes a model coupling approach that uses a market-based mechanism to efficiently coordinate the interactions among electricity, heat, and (hydrogen) gas systems, and (near) optimally schedule flexibility to maximize social welfare. The proposed approach is benchmarked against traditional co-optimization, and is shown to achieve comparable results with a moderate "optimality gap"in terms of reduction in system costs, peak load, and VRE curtailment. Its added value is the ability to enable each system to interact in an integrated energy system and locally optimize their decisions without sharing confidential information. The practical implication of this new approach is to provide a modeling environment where system operators and flexibility aggregators can obtain insights into the impacts of decarbonization of other parties on their systems - thereby avoiding myopic operational or investment decisions.

Decarbonisation of the electricity sector has led to the adoption and deployment of a large number of consumer-sited flexible assets. Simultaneously, consumers are becoming increasingly aware of their consumption patterns and are eager to reduce their energy expenses making demand response a significant source of flexibility in energy markets. In this paper, we discuss the policy measures that influence a consumer's ability to respond to price signals and offer flexibility in the day-ahead market. We propose two methods to quantitatively analyse these policy instruments through their inclusion in market clearing models for the Dutch day-ahead power market. A single-level optimisation model with social welfare maximisation objective can be used to perform a simplified assessment of changes in demand bids due to policy-based financial influences. This model is suitable for studying simple policies such as time-independent taxes but unsuitable for complex policies such as network tariffs and subsidies. A bi-level optimisation model with consumer surplus maximisation on the upper level and social welfare maximisation on the lower level allows more sophisticated modelling of policies but is limited by its scalability and computational complexity. The two methods can be compared on the basis of their ability to incorporate different policy instruments and market design choices, model consumer bidding behaviour, their computational complexity and challenges to implementation.

Energy system integration promises in-creased resiliency and the unlocking of synergies, while also contributing to our goal of decarbonization. It is enabled by both old and new technologies, glued together with data and digital services. Hydrolyzers, heat pumps, distributed renewable generation, smart buildings, and the digital grid edge are all currently the subject of integration with the power system and the energy sector at large. To plan and operate such a multidisciplinary and multisectoral system properly, insight, tools, and expertise are all needed. This is exactly where the state of the art fails to deliver: tools for integrated energy systems (IESs) are still in their infancy, and many times, even academia treats these sectors separately, producing experts in each of them but not across.

Electrifying the utility supply of existing petrochemical processes is a potential measure for CO₂ emission reduction in the chemical industry. With an increasing share of variable renewable energy sources in the electricity grid, electricity price fluctuations will become more frequent. However, most existing petrochemical processes operate continuously and, therefore, require a constant supply of utilities. In this paper, we model an electrified utility system that includes different types of storage units to explore how a constant utility demand could be supplied under fluctuating electricity prices. To achieve this, we model a utility system that provides electricity and heat to an olefins plant in the Port of Rotterdam and use mathematical optimisation to capture optimal hourly operations of the plant under fluctuating prices. We find that the cost-optimal utility system consists of electric boilers, integrated thermal energy storage, and technologies for storing and using hydrogen produced on-site. With data for prices of the Dutch electricity grid in 2022, the electrified utility system results in higher costs than a fossil-based system. Increasing price fluctuation levels would lead to lower operational costs as the system's flexibility enables shifting the electricity consumption to the hours with the lowest electricity prices.

The extensive integration of distributed renewable energy resources (DRES) can lead to several issues in power grids, particularly in distribution grids, due to their inherent intermittency. This paper presents a stochastic simulation-based approach to estimate the maximum permissible penetration level of DRES and to determine the optimal capacity of centralized battery energy storage systems (BESS) in distribution networks while adhering to technical constraints. The stochastic method creates a wide range of scenarios under various conditions. For each scenario, our proposed approach calculates the maximum allowable penetration level of DRES and the required BESS capacity with different DRES control logics. The maximum allowable penetration level of DRES and the requirements of the BESS capacity are determined by an analysis of various simulation results. This paper's unique contribution lies in equipping distribution system operators (DSOs) with the ability to compare results and select the most appropriate voltage control and power smoothing methods. This aids in mitigating challenges associated with overvoltage and intermittency issues arising from DRES-generated power, thereby enhancing the overall resilience and reliability of the power grid. Case studies that include four voltage control algorithms and three power smoothing methods demonstrate the universality and effectiveness of the proposed approach.

The increasing proportion of renewable energy introduces both long-term and short-term uncertainty to power systems, which restricts the implementation of energy management systems (EMSs) with high dependency on accurate prediction techniques. A hierarchical online EMS (HEMS) is proposed in this paper to economically operate the Hybrid hydrogen–electricity Storage System (HSS) in a residential microgrid (RMG). The HEMS dispatches an electrolyzer-fuel cell-based hydrogen energy storage (ES) unit for seasonal energy shifting and an on-site battery stack for daily energy allocation against the uncertainty from the renewable energy source (RES) and demand side. The online decision-making of the proposed HEMS is realized through two parallel fuzzy logic (FL)-based controllers which are decoupled by different operating frequencies. An original local energy estimation model (LEEM) is specifically designed for the decision process of FL controllers to comprehensively evaluate the system status and quantify the electricity price expectation for the HEMS. The proposed HEMS is independent of RES prediction or load forecasting, and gives the optimal operation for HSS in separated resolutions: the hydrogen ES unit is dispatched hourly and the battery is operated every minute. The performance of the proposed method is verified by numerical experiments fed by real-world datasets. The superiority of the HEMS in expense-saving manner is validated through comparison with PSO-based day-ahead optimization methods, fuzzy logic EMS, and rule-based online EMS.