The energy transition encourages using heat pumps at the residential level, which results in a multi-carrier energy system when combined with PV and battery storage. Optimally controlling such systems has proven challenging. The numerous constraints required, different response times per energy carrier, and the need for forecasting methods also increase the complexity and computational cost. We propose an adaptable energy management system strategy for any system architecture with a reduced number of constraints using genetic algorithms with a discrete-continuous approach for the power setpoints. Using random forest regression, we also created short-term estimation models for the PV generation and electric and thermal demand, with error distributions centred near 0 %. Our results demonstrate that the strategy can solve the power allocation problem in the order of 1 s, including forecasting 60 minutes, minimizing electric costs, and ensuring thermal comfort.

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.

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.

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.

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.

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.

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.

Anticipating failures is vital for maintaining a reliable power supply. Advanced measurement devices in the grid generate vast data that contains valuable information on grid operations. Initial signatures of an incipient failure are often reflected in this data in the form of electrical waveform distortions. Conventional protection schemes are not equipped to analyze these distortions and anticipate failures. There is a considerable research gap for a simple yet robust and universal failure anticipation and diagnosis scheme. This paper proposes a universal Failure Anticipation and Diagnosis Scheme (FADS) to detect incipient failures in AC distribution grids. The method comprises three short stages, helping the operator make an informed decision. In the first stage, the FADS scheme leverages the fundamental properties of electrical sinusoid waveforms to detect distortions. In the second stage, the distortion data is processed through pre-determined thresholds set in accordance with the system's regular operation. In the third stage, depending on the system, the FADS uses the extent of the violations of these thresholds and ranks the severity of the danger posed to grid operations. The classification helps determine if the waveform distortions are the signature of an incipient failure. The proposed FADS method's reliability, robustness and effectiveness are evaluated in incipient failure conditions of field events modelled in real-time simulations on standardized IEEE distribution feeders. The FADS is a high-speed distortion detector, is quite sensitive, and the method has high selectivity because of its nature.

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.

Future power systems, in which generation will come almost entirely from variable Renewable Energy Sources (vRES), will be characterized by weather-driven supply and flexible demand. In a simulation of the future Dutch power system, we analyze whether there are sufficient incentives for market-driven investors to provide a sufficient level of security of supply, considering the profit-seeking and myopic behavior of investors. We co-simulate two agent-based models (ABM), one for generation expansion and one for the operational time scale. The results suggest that in a system with a high share of vRES and flexibility, prices will be set predominantly by the demand’s willingness to pay, particularly by the opportunity cost of flexible hydrogen electrolyzers. The demand for electric heating could double the price of electricity in winter, compared to summer, and in years with low vRES could cause shortages. Simulations with stochastic weather profiles increase the year-to-year variability of cost recovery by more than threefold and the year-to-year price variability by more than tenfold compared to a scenario with no weather uncertainty. Dispatchable technologies have the most volatile annual returns due to high scarcity rents during years of low vRES production and diminished returns during years with high vRES production. We conclude that in a highly renewable EOM, investors would not have sufficient incentives to ensure the reliability of the system. If they invested in such a way to ensure that demand could be met in a year with the lowest vRES yield, they would not recover their fixed costs in the majority of years.

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 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.

Electrification of processes and utilities is considered a promising option towards the reduction of greenhouse gas emissions from the chemical industry. Therefore, electricity demand is expected to increase steeply. Since the sources of future low-carbon electricity are variable in nature, there is a need for strategies to match availability and demand. Literature identified the flexibility of chemical processes as one promising strategy to address variability. This study aims to provide insights into how stakeholders from the power sector and the chemical industry consider flexibility in chemical processes and to identify key benefits and bottlenecks. For this article, we combined a review of peer-reviewed and grey literature with stakeholder interviews to map and describe the state of the art of flexible chemicals production, and to identify requirements for further research. The main drivers to investigate the flexibility potential are first, the contribution to energy system reliability, and second, potential cost savings for the industry. Main limitations are considered to be first, the uncertain economic performance of flexible processes due to investment costs, reduced production and uncertain revenues from flexible operation, and second, the complexity of the implementation of flexibility.

This paper introduces a flexible and extendable easy-to-use energy system integration development kit: the Illuminator. The Illuminator illustrates challenges arising from the energy transition. Hence, it is suitable in education and for demonstration. It also acts as a sandbox for testing new research concepts, and particularly, distributed energy coordination algorithms in real and non-real time. The Illuminator technology is primarely a modular software platform developed to run on a Raspberry Pi (RasPi) cluster. It is open-source, available at GitHub and developed in Python. The Illuminator comprises models of common energy technologies, such as photovoltaic (PV) panels, wind turbines, batteries, and hydrogen systems. The uniqueness of the Illuminator is in its modularity and flexibility to reconfigure scenarios and cases on the fly, even by non-experts in a plug-and-play fashion. This paper introduces the Illuminator and shows its performance in a simple case study.

A fuzzy logic based online energy management system (FLEMS) is designed in this paper to achieve the optimal electricity cost in a residential Microgrid (MG). The proposed FLEMS is combined by a local energy price model (LEPM) and a fuzzy-logic strategy. The LEPM will preprocess the sampling data to estimate the electricity market and local MG status. The fuzzy-logic mimics the artificial intelligent assessment to economic issues and make decision for the charging and discharging operation for energy storage system (ESS). In the FLEMS, not only electricity price and supply-demand balance, but also ESS state of charge are considered for the efficient and stable operations. The proposed method does not relay on the accurate prediction of renewable energy source and local loads. Historical experience of the system is involved by the LEPM and guides the ESS operation in the fuzzy-logic. A real-world data based household-level residential MG model is established to validate the performance of the FLEMS. A hourly-resolution-Particle swarm optimization (PSO) with perfect day-ahead prediction is implemented as the baseline to verify the superiority of the proposed method.

This paper deals with a systematic assessment of the power system frequency dynamics under high penetration of converter-interfaced renewable energy sources (CI-RESs). Specifically, the concept of the virtual synchronous generator (VSG) is implemented in the CI-RESs located at the transmission system (TS) side and/or the distribution network (DN) side. Dynamic RMS simulations are performed on a testbed consisting of the IEEE 9-bus TS grid and the CIGRE medium-voltage DN grid under different CI-RES penetration levels and VSG control parameters to assess the VSG impact on the power system frequency dynamics. It is shown that the decommissioning of conventional power plants coupled via synchronous generators can be safely performed in case the VSG concept is adopted correctly.

As the penetration of Converter-Interfaced Dis-tributed Renewable Energy Sources (CI-DRES) increases, several problems are revealed in electric power systems, e.g., power quality issues, reverse power flows and frequency instability. A solution to tackle these issues is the mitigation of high CI-DRES active power ramp-rates (RRs) by utilizing energy storage systems (ESS). In many grid-codes at transmission system (TS) level, it is specified that the CI-DRES limit their RRs, while also the utilization of a central ESS has been proposed to limit the RRs. Nevertheless, this approach involves only large energy market players. Although various RRL methods have been proposed for CI-DRES, a remaining gap is the evaluation of the RR of a Distribution Network (DN) containing CI-DRES and loads together with the influence of distributed ESS in the DN. Towards this direction, in this paper, this evaluation is performed in order to study the RRL capability of a low-voltage (LV) DN considering both central and distributed ESS. The analysis is conducted in the LV CIGRE DN via quasi-steady-state and RMS simulations in PowerFactory considering several techno-economic parameters, e.g., ESS size, type, per unit cost. This evaluation will help towards the integration of the RRL control in the grid codes in DNs so that it can be considered as a new ancillary service to be remunerated in respective markets where also small CI-DRES owners will be able to participate.

As the penetration of distributed energy resources (DERs) increases significantly in the distribution networks, their integration reshapes distribution system power flows and leads to serious voltage quality problems in distribution networks. In this paper, a novel distributed cooperation voltage regulation method is proposed for future distribution networks. The proposed method can minimize the number of agents involved in the voltage regulation and it minimizes the change of required power for voltage regulation, which together minimizes the need for re-dispatching, i.e. the impact of voltage regulation on exchange of energy. Moreover, the proposed method performs online optimization, i.e the value of the decision variable is physically implemented as a controller set-point at each iteration which reduces the response time. The presented algorithm is benchmarked against ADMM and centralized optimization. The results show the voltage regulation effectiveness. Compared to the centralized method, the proposed method performs better in the case of a single agent failure, and compared to the ADMM method, the proposed method greatly reduces the action time and does not require retuning if network topology or agent participation changes.

The traditional methodology for conducting technical assessments of multi-energy systems involved using domain-specific modeling tools to focus on the energy sector of interest, while making simplifying assumptions about any coupled energy sector. This was acceptable since the interactions between energy domains were minimal. However, with the expectation of an increased adoption of energy conversion technologies (such as power to X (P2X) systems: power to heat, power to gas, etc.) in the future, and consequently higher interaction between various energy sectors and stronger dependence on one another, there is a need to update the current method for conducting technical assessments. This means taking into account not only the energy sector of interest, but also any dependent energy sectors, and the associated energy transformers (P2X). In this paper, we propose a co-simulation based approach to conduct simulation-based technical assessments of multi-energy systems, which allows us to couple domain specific modeling tools. We re-introduce the tool ENERGYSIM to conduct the multi-energy system co-simulations. We motivate the importance of the proposed tool and compare it with other available tools. We highlight its main functionalities, and using a study case, we show how a multi-stakeholder, multi-energy system co-simulation can be set up and assessed.

A numerical model of a power system can be used to get accurate insights into the impact of policies and investment decisions regarding the transformation of the energy system, while also helping in identifying bottlenecks in implementing decisions. Spatial aggregation, especially for generation and load, must be carefully approached to obtain such a valid model of a power system. The two main contributions of this paper are introducing a valid model of the Dutch high-voltage power system based on open data and open-source software, and proposing a method for spatially aggregating generation and load capacities to high-voltage nodes of the power system. The representative model will enable interdisciplinary research on policy-making and investment decisions specific to the Netherlands.