Interconnected Energy Systems Research Articles

Energy optimization algorithms for multi-residential buildings: A model predictive control application

This study presents an optimization algorithm for Model Predictive Control (MPC) of the HVAC systems in multi-family residential buildings assessing the performance of four objective functions. Implemented in C++, using the free OR-Tools optimization library, the model is formulated a Mixed Integer-Linear Programming (MILP) problem. The study analyses the results of tests conducted on a 20-dwelling block in Switzerland across various weather and occupancy conditions, resulting in a parametric study of 64 cases.The models developed for the MPC are Grey-box type for the interconnected energy systems: the building, thermal storage tanks, a heat pump, the ventilation system and PV collectors, highlighting a radiant wall heating system integrated into the building facade. The tanks and the heat pump models were informed with manufacturer data, while for the building a R3C3 thermal-electrical equivalent model was developed, calibrated using TRNSYS simulations with a root mean square error of 1.7%.Findings demonstrate how the algorithm optimizes the operation according to the desired criteria, while ensuring indoor comfort with a 15-minute time resolution. The time execution of the majority of cases is under 3 min in a low-specs computer, affirming its practical viability for real-world implementation.

Research on low-carbon optimization methods for regional energy interconnection systems considering multi-energy coupling characteristics

Abstract With the increasing application of multi-energy cogeneration, distributed renewable power generation, and multi-energy storage in various multi-energy systems, their efficient collaborative optimization has become the key to further improving emissions. This article utilizes energy hubs to integrate energy equipment with multi-energy coupling and multi-source supply characteristics and constructs a regional interconnected energy system that considers multi-energy coupling characteristics. A multi-objective optimization model for the above-mentioned energy system is established, including economic, low-carbon, and safe aspects such as operating costs, process emissions, and operational safety, and the raccoon optimization algorithm is applied to solve the model. The experimental results show that compared with classical scheduling algorithms, the proposed method can effectively reduce carbon emissions at a certain operating cost.

Optimizing power quality in interconnected renewable energy systems: series active power filter integration for harmonic reduction and enhanced performance

Optimizing power quality in interconnected renewable energy systems: series active power filter integration for harmonic reduction and enhanced performance

Dynamic control of district heating networks with integrated emission modelling: A dynamic knowledge graph approach

This paper presents a knowledge graph-based approach for the dynamic control of a district heating network with integrated emission dispersion modelling. We propose an interoperable and extensible implementation to forecast the anticipated heat demand of a municipal heating network, minimise associated total generation cost based on a previously devised methodology, and couple it with dispersion simulations for induced airborne pollutants to provide automatic insights into air quality implications of various heat sourcing strategies. We create cross-domain interoperability in the nexus of energy and air quality via newly developed ontologies and semantic software agents, which can be chained together via The World Avatar dynamic knowledge graph to resemble the behaviour of complex systems. Furthermore, we integrate the City Energy Analyst into this ecosystem to provide building-level insights into energy demand and renewable generation potential to foster strategic analyses and scenario planning. Underlying calculations use building and weather data from the knowledge graph in place of inherent assumptions in the official software release, facilitating a more data-driven approach. All use cases are implemented for a mid-size town in Germany as a proof-of-concept, and a unified visualisation interface is provided, allowing for the examination of 3D buildings alongside their corresponding energy demand and supply time series, as well as emission dispersion data. With this work, we outline the potential of Semantic Web technologies to connect digital twins for holistic energy modelling in smart cities, thereby addressing the increasing complexity of interconnected energy systems.

Multi-Energy Flow Integrated Energy System Considering Economic Efficiency Targets: Capacity Allocation and Scheduling Study

An integrated energy system (IES) breaks down barriers between different energy subsystems, enhancing energy reliability and efficiency. However, issues such as uneven equipment capacity allocation and suboptimal scheduling persist in multi-energy flow IES. To maximize economic benefits while ensuring energy balance and the operational characteristics of the equipment, a capacity matching optimization and scheduling strategy model for IES was developed. Firstly, mathematical models for the electricity, gas, and thermal networks within the IES were established. Secondly, considering the efficiency of energy conversion between different forms and constraints of energy storage in the electricity–thermal–gas interconnected energy system, optimization solutions were obtained using regional contraction algorithms and sequential quadratic programming methods. Finally, case studies conducted in a real park demonstrated that, through optimized capacity matching, unit prices for electricity, heat, and gas decreased by 39.9%, 90.5%, and 74.2%, respectively, effectively improving the economic viability of the system.

Two-Layered Hierarchical Optimization Strategy With Distributed Potential Game for Interconnected Hybrid Energy Systems.

Due to the existence of different stakeholders, it makes competitive game characteristic in hybrid energy systems (HESs). Combined with the high-dimensional complexity and output uncertainty of distributed energy resources, the optimal operation of HESs can be a more challenging problem. Here, this article proposes a potential game-based two-layered hierarchical optimization strategy to deal with this problem. With consideration of its high-dimensional complexity, a two-layered hierarchical HES model is created, consisting of an upper-level and a lower-level model. For properly solving competitive relationships among different stakeholders in the upper-level model, a multiagent system for stakeholders is created and a potential game is employed with a distributed primal-dual perturbed algorithm, and its convergence and optimality have been both proved. Moreover, an uncertainty and robustness analysis is done with coordination between lower and upper models, which deduces a feasible robust uncertainty interval in the lower-level model. For better dealing with the lower-level model, a gradient descent-based multiobjective differential evolution (GD-MODE) algorithm is utilized to optimize the economic cost and emission issue simultaneously, producing a set of Pareto-optimal schemes. Combined with simulation results, it is proven that the proposed method can reduce computational complexity as well as properly deal with uncertainty problems for the optimal operation of HESs.

Fit for purpose: Modeling wholesale electricity markets realistically with multi-agent deep reinforcement learning

Electricity markets need to continuously evolve to address the growing complexity of a predominantly renewable energy-driven, highly interconnected, and sector-integrated energy system. Simulation models allow testing market designs before implementation, which offers advantages for market robustness and efficiency. This work presents a novel approach to simulate the electricity market by using multi-agent deep reinforcement learning for representing revenue-maximizing market participants. The learning capability makes the agents highly adaptive, thereby facilitating a rigorous performance evaluation of market mechanisms under challenging yet practical conditions. Through distinct test cases that vary the number and size of learning agents in an energy-only market, we demonstrate the ability of the proposed method to diagnose market manipulation and reflect market liquidity. Our method is highly scalable, as demonstrated by a case study of the German wholesale energy market with 145 learning agents. This makes the model well-suited for analyzing large and complex electricity markets. The capability of the presented simulation approach facilitates market design analysis, thereby contributing to the establishment future-proof electricity markets to support the energy transition.

Comparative Investigation of On-Grid and Off-Grid Hybrid Energy System for a Remote Area in District Jamshoro of Sindh, Pakistan

To meet electricity requirements and provide a long-term, sustainable existence, remote areas need to promote renewable projects. Most of the time, wind and solar power sources are selected as renewable energy technologies to help satisfy some of the power requirements. Alternative approaches should be employed, considering the inconsistent characteristics among those resources, to offer efficient and long-lasting responses. Electricity production needs to be conducted with the help of a wide range of energy sources to be productive and efficient. As a result, the current research concentrates on feasible analyses of interconnected hybrid energy systems for such remote residential electricity supply. To help a remote area’s establishment decide whether to adopt renewable electricity technology, this paper evaluates the techno-economic effectiveness of grid-connected and standalone integrated hybrid energy systems. The electricity requirements for the entire selected remote area were determined first. Furthermore, the National Aeronautics and Space Administration, a national renewable energy laboratory, was used to evaluate the possibilities of green energy supplies. A thorough survey was performed to determine which parts were needed to simulate the interconnected hybrid energy systems. Employing the HOMER program, we conducted a simulation, optimizations, and economic research. Considering the net present cost, cost of energy, and compensation time, an economic comparison was made between the evaluated integrated hybrid systems. The assessment reveals that perhaps the grid-connected hybrid energy system is the best option for reliably satisfying remote areas’ energy needs.

A Novel Swarm Approach for Regulating Load Frequency in Two-Area Energy Systems

One of the most important strategies for running and controlling an electric power system is the load frequency controller. LFC can be used to solve a variety of issues, such as when a generating unit is rapidly turned off by protection equipment or when a heavy load is quickly connected or disconnected. When disturbances disrupt the natural power balance, the frequency deviates from what it should be. LFC is in charge of balancing the load and restoring the natural frequency to its proper level. In this case, load frequency control optimization techniques are used in the Multiple Connect Area System to provide reliable and quality operation on frequency and tie line power flow. The purpose of this paper is to demonstrate how optimising LFC in a two-area interconnected energy system with hydro, thermal plants, and a particle swarm optimization (PSO) method may improve power system stability and save revenue on power generation. A standard (PID) controller is used to control the system. The PSO optimization approach is utilised to determine the optimal gain values of the controllers kp, ki, and kd.

Acausal equation-based and object-oriented modeling of heating systems: The College Thermal library

With the growing demand for adequate thermal comfort and increasingly sophisticated and interconnected energy systems, software tools able to easily and effectively study complex thermal systems need to be developed. Here, a novel acausal equation-based and object-oriented modeling (OOM) approach is used to model the seasonal performance of a residential heating system taken as a case study. To this end, a purpose-built library named College Thermal was developed using Modelica language in the Wolfram System Modeler simulation environment. In detail, dynamic simulations under varying operating conditions were performed to assess the ability of the system to ensure the design conditions, and to estimate the resulting energy consumption during the heating season. Then, the effect of key parameters of the thermal components on the overall energy performance of the system is analyzed. The developed OOM library, which is characterized by interoperability, modularity, and scalability features and thus could easily incorporate extensions/evolutions of the studied system, would be prospectively suitable for optimizing system layouts and testing control strategies in various thermal engineering applications.

Turkmenistan Afghanistan Pakistan India Gas Pipeline and Foreign Policy of Pakistan

Turkmenistan-Afghanistan-Pakistan India gas pipeline project is a component of an extensive plan to develop Asia’s interconnected energy systems. The Turkmenistan-Afghanistan-Pakistan pipeline has a significant impact on energy security and regional integration in South Asia. This gas pipeline project can address the growing energy demand of the region. However, due to the volatility in the political landscape of South Asia and the complexity of large-scale energy projects, the project is facing a number of critical challenges that need the focus of the regional partners and stakeholders. The limited literature on the TAP gas pipeline project has focused on security hurdles to the projects from the member countries’ perspective. The research study found that the main hurdle to executing the TAP gas pipeline project in Pakistan is the country’s internal issues, a lack of investments, lack of financial resources, international pressure, the country’s regional issues and the dynamics of changing regional situations. The research study is proposed to be useful for policymakers to evaluate the implication of energy import dependency and to frame foreign policy.

Cascaded heat merit order for industrial energy systems to evaluate district heating potential

Industrial companies are undergoing a transformation to decrease energy costs and reduce emissions. The integration of renewables, sector coupling technologies, and industrial waste heat lead to complex interconnected industrial energy systems. As district heating systems play a decisive role for the integration of industrial waste heat in the building sector, barriers must be analyzed to overcome the gap between waste heat potential and waste heat use. Although data from production systems and their on-site energy supply are becoming available in the ongoing process of digitization, information deficits can be identified as one of the main barriers to couple industrial energy systems with district heating. We present a data-based methodology to evaluate the potential of industrial energy systems for connecting to district heating systems. Data from production systems, energy converters, thermal networks and necessary parameters of district heating systems are merged into a data model to determine a cascaded heat merit order and indicators for the energetic, economic and ecological potential. To set up the cascaded heat merit order, an algorithm for balancing complex industrial energy systems is integrated within the data model. In a case study, we apply the methodology to data of an industrial site. Besides increasing transparency through visualization of the cascaded heat merit order and corresponding indicators, the results show a base load potential of up to 0.8 MW over a year.

Control of Multicarrier Energy Systems from Buildings to Networks

Cost, efficiency, and emissions concerns have motivated the application of advanced control techniques to multiple carrier energy systems. Research in energy management and control over the last two decades has shown that significant energy and CO2 emissions reductions can be achieved. Within the last decade, this work has expanded to the domain of interconnected energy systems. The interconnection control of multiple energy carriers, conversion devices, and energy storage provides increased flexibility and energy/CO2 reduction potential. The focus of this article is on outlining the control methods required for these systems over a range of energy consumption and timescales. Dynamic interactions between multicarrier systems occur over timescales ranging from 15 minutes to seasons. The constrained nature of the resulting control problems favors optimization-based approaches.

Economic Emission Generation Scheduling of Interconnected Energy System Using Hybrid Optimization Technique

Economic Emission Generation Scheduling of Interconnected Energy System Using Hybrid Optimization Technique

Energy: life, power, and livelihood

We and our habitat are forms of energy, endowed with life, and irrevocably earthlings. A cosmological-to-locality appreciation of energy in food and nutrition science, practice and policy is overdue. Our livelihood, and any function we have, are energy dependent, as is planetary habitability. Energy cognisance has become an existential necessity in an increasingly self-destructive ‘anthropocene’ era when our own energy equilibrium is compromised. This is manifest in destruction and loss of our ecology, our livelihood expectations, and practices, and, in turn, our wellbeing and health. Most problematic has been the domination, skewing and loss of biomass caused by humans, both their over-population of the earth, and their exploitation of its natural resources. These resources provide fuel for warmth, cooking and transport, textiles, and clothing; are subject to land and aquatic harvest, are replaced by dwellings and infrastructural buildings, and yet are recreational assets. Wastage has been of scant regard. Energy misuse besets the entire food system. This has followed the development of the wood and coal-fired steam engine, the advent of gasoline powered internal combustion engines, use and transmission of electricity, and an insatiable arms industry. Now, we are at the brink of extinction. Profiteering and conflict over energy control has fostered unfettered industrial materialism, a major extinction risk factor. Not only is energy the power we need, but it has also underwritten the powerful. Can we be sufficiently insightful and collaborative to change this energy trajectory and survive healthfully on a habitable planet? Individuals, households, and communities, as opposed to unaccountable monopolies, could achieve control of the energy systems on which our livelihoods depend and render them sustainable, accessible, and affordable. Interconnected food and energy system ownership could be devolved to ‘The Commons’ as a cooperative, sustainability strategy. The social momentum and appropriate technology for energy conservation, renewability and personalisation is now available for mobilisation to address our food, nutrition, and health insecurity.

Hydrogen Deep Ocean Link: a global sustainable interconnected energy grid

The world is undergoing a substantial energy transition with an increasing share of intermittent sources of energy on the grid, which is increasing the challenges to operate the power grid reliably. An option that has been receiving much focus after the COVID pandemic is the development of a hydrogen economy. Challenges for a hydrogen economy are the high investment costs involved in compression, storage, and long-distance transportation. This paper analyses an innovative proposal for the creation of hydrogen ocean links. It intends to fill existing gaps in the creation of a hydrogen economy with the increase in flexibility and viability for hydrogen production, consumption, compression, storage, and transportation. The main concept behind the proposals presented in this paper consists of using the fact that the pressure in the deep sea is very high, which allows a thin and cheap HDPE tank to store and transport large amounts of pressurized hydrogen in the deep sea. This is performed by replacing seawater with pressurized hydrogen and maintaining the pressure in the pipes similar to the outside pressure. Hydrogen Deep Ocean Link has the potential of increasing the interconnectivity of different regional energy grids into a global sustainable interconnected energy system.

Nexus-e: A platform of interfaced high-resolution models for energy-economic assessments of future electricity systems

Energy systems are transitioning toward sustainable power generation largely due to new policies that are motivated by climate and renewable generation targets. This transition is felt throughout the entire economy and is affecting the long and short term operations of the energy system. In the last decade, the research community has made significant efforts to model the energy transition and its impacts. Typically, these models focus on a limited subset of the different components of the energy-economic system, including investments in centralized and distributed generation, electricity markets, electric power grids, security of supply, and macro-economic effects. However, there are research questions that require the analysis of how these different aspects are connected to each other and how they may interact. Therefore, the research community has stressed the need to leverage models across domains to enhance the capabilities and robustness of their results. Due partly to the high complexity of combining models developed in tangential domains, few attempts have been made to model in full the interactions among the interdependent components of the energy-economic system. The Nexus-e: interconnected energy systems modeling platform aims to show how this gap can be filled by demonstrating that an interdisciplinary set of models can be integrated in a model framework by linking them through structured interfaces. This platform combines four bottom-up models that capture different aspects of the electricity system and one top-down macro-economic model to represent a much broader scope of the energy-economic system as compared to traditional stand-alone modeling approaches. In this paper, we study the benefits and limitations of the interfaces established among the modules in Nexus-e with reference to the Swiss electricity system in a European context. We demonstrate that prominent changes in operational behavior could drive investments and should therefore be assessed in a framework that considers both transmission and distribution systems in a coordinated manner. We show that the bottom-up investments and operations (transmission and distribution) can have an impact on the overall economy, which in response can affect the demand for electricity. Moreover, we demonstrate that the changes in generation mix and operation behaviors may have a significant impact on system security. For policymakers, the approach can provide spatially detailed power system transformation options that enable decisions that are socially, politically, and technically acceptable.

Optimizing the renewable and fossil-fired generation capacities: Case study of interconnected district-level systems

We first comprehensively analyze determining the generating capacities of each type, then discuss the heat distribution across interconnected district-level heat energy systems (“the hubs”) containing both low-scale and centralized heat plants; discussion is presented from the standpoint of primary energy consumption and costs. The applied tool includes a flexible heat generation model and an accurate objective function. The set of constraints and the Jacobian matrix are configured manually. We apply the fixed-point iteration approach to solve an equation system. In order to evaluate the consequences of all possible scenarios, we analyze a novel district heating (DH) system with low-scale solar collectors and heat pumps against central combined heat-and-power (CHP) plant fueled with fossil fuels and biomass. The proposed system is evaluated from the standpoint of the state-of-the-art, future prospects, and environmental impact. For each specific type of capacities, we herein present maximum achievable net generation of heat. System layout sketch is based on the data on the actual districts of Canberra, Australia. All the hubs are studied as a whole with due account of heat redistribution. The configuration made implies construction renewable energy facilities with a heat generation of 6.5+ MW, which will suffice to cover about a fifth of the peak load. This approach is quite novel for Australia. With greater electricity and heat supplies, renewable resources can cover an even greater share of such supplies. To sum up, a significant difference between the costs of a natural gas-based DHS and a coal-fired DH one lies in the fixed costs of operating the CHPPs and in the fuel costs. The self-generation of a hub may be inconsistent with the total energy balance, which is the total of eigen-production, energy inflows and outflows. The total system performance is considerably better when coal-fired CHPPs account for the bulk of net production.

Determination of the Average Specific Capital Consumptions of CCPP with Gas Turbine of 30-125 MW and Put into Operation at TPPs of the Russian Federation from 2015 to the First Quarter of 2021

Research was carried out on the volume of gas turbine commissioning in the range of unit capacities of 30–125 MW at TPPs of the Russian Federation operating as part of a CCPP in the period from 2015 to the first quarter of 2021. The purpose of the research was to determine the average specific capital investment in construction and the average specific fuel consumption for the supply of electricity and heat for the CCPP introduced over the years in the united energy systems of the Russian Federation. Gas turbines are classified by electrical capacity into three groups: from 100 to 125 MW, from 60 to 99 MW and from 30 to 59 MW. An assessment of the quantitative distribution of gas turbines over the interconnected energy systems of the Russian Federation has been carried out. The results of the comparison of the quantitative commissioning of gas turbines are shown in the period from 2010 to the economic crisis of 2014 and in the period from 2015 to the present. A preliminary estimate of the increase in average specific capital investments in the construction of CCPP, which included gas turbines of the same electric power, was made for these periods.

Risk-profit analysis of regional energy service providers by regularized primal-dual interior point method

With the development of the electric market and smart grid, risk management is one of the key issues of optimal dispatch in the interconnected energy system. The uncertainty of market price will lead to the risk for the profit of the regional energy service providers (RESP). This paper firstly defines conditional robust profits (CRP) based conditional value-at-risk and uses it constructs a risk-averse model of a multi-regional interconnected energy system, which includes generator units, demand response, and energy storage system, model named as CRP-RESP. Then by building upon the notion of effective scenarios, the effective scenarios reduction technology (ESRT) is used to reduce the scale of the CRP-RESP model effectively. Besides, efficiently solve CRP-RESP and realize the privacy protection of RESP, CRP-RESP is decomposed by geographical region, and distributed computing is carried out by using regularized primal–dual interior point method (R-PDIPM) with disturbance terms. Finally, the effectiveness and reliability of the risk-averse model are tested with the case study. ESRT can not only effectively reduce the scale of the CRP-RESP model, but also obtain a high-quality risk scheduling scheme compared with other clustering methods. And the convergence ability of R-PDIPM with disturbance terms is verified from both theoretical and experimental aspects.