limits and potentials of mixed integer linear programming methods for optimization of polygeneration energy systems

Abstract The simultaneous production of different energy vectors from hybrid polygeneration plants is a promising way to increase energy efficiency and facilitate the development of distributed energy systems. The inherent complexity of polygeneration energy systems makes their economic, environmental and energy performance highly dependent on system synthesis, equipment selection and capacity, and operational strategy. Mixed Integer Linear Programming (MILP) is the state of the art approach to tackle the optimization problem of polygeneration systems. The guarantee of finding global optimality in linear problems and the effectiveness of available commercial solvers make MILP very attractive and widely used in optimization problems of polygeneration systems. Nevertheless, several drawbacks affect the MILP formulation, such as: the impossibility of taking into account nonlinear effects; the necessity of considering all the time periods at once; the risk of high-dimensionality of the problem. To tackle these limitations, several techniques have been developed, such as: piecewise linearization methods; rolling horizon approaches; dimensionality reduction by means of energy demands clustering algorithms. In this paper, limits and potentials of MILP methods for the optimization problem of polygeneration energy systems are reviewed and discussed

Simultaneous sizing and energy management of multi-energy virtual power plants operating in regulated energy markets

This research analyses the case of a Virtual Power Plant (VPP) in regulated electricity markets, trading energy with the consumers and the grid under a Power Purchase Agreement (PPA). The VPP propagates the deployment of solar PVs while balancing its intermittency with a dispatchable power plant, which is assumed in this research to be a CCHP, supplying cooling, heating, and power. The VPP also integrates energy storage systems for a comprehensive assessment. Traditionally, the VPP concept has not been introduced in regulated markets, but it is widely researched in deregulated markets where VPPs trade energy with the electricity grid for profit maximisation. In regulated markets, a special architecture is proposed for a VPP that mediates between residential compounds and electricity grids for profit maximization and energy demand coverage, thus converting the compound into a power generator with minimum dependence on the grid for its energy demand. In the literature on aggregated energy systems in regulated markets, it is usually overlooked to perform detailed energy modelling and optimisation on an hourly level. Only basic rule-based frameworks for energy management are proposed. In this research, it is initially assumed that since the VPP integrates multi-energy components supplying heating, cooling and electricity, optimization of the output of each component for a common profit maximization, is necessary. However, in VPP-related literature, the capacity of each component, which is a main input for energy modelling, is traditionally assumed and not assessed. Therefore, the research aims to explore how to find the optimal capacity configuration of the residential VPP that achieves optimal profit. The paper analyses an iterative exhaustive search framework, integrating the 2-levels of energy optimisation (hourly profit maximisation objective) and capacities optimisation (Life cycle CAPEX & OPEX minimisation). Compared to baseline cases, where only energy optimisation is performed, and capacities are assumed and not assessed in terms of capital investment, the proposed framework achieved a higher annual profit by 3.1% and a payback period of 11 years. The results also provide comprehensive 3D charts drawing the relations between the achieved profit against capacities configurations, thus allowing high-level decision-making. The results also prove the hypothesis that hourly energy optimisation should not be performed without investment cost assessment and that targeting the minimization of investment costs will indirectly benefit the achieved profit

Integration of Distributed Energy Storage into Net-Zero Energy District Systems: Optimum Design and Operation

A net-zero energy district is any neighborhood where the consumption of the buildings is offset by on-building generation on an annual basis. In this study, a net-zero energy district is identified among the set of optimal solutions and the effects of storage on its performance is investigated. It is assumed the model simultaneously optimizes the location of host buildings (energy generators), type of technologies and associated size, and the energy distribution network layout together with the optimal operating strategy. The optimization model addresses all technologies with a special focus on the effect of application of energy storage. Two types of energy storage are considered inside each building: thermal energy storage (hot water tank) and electrical energy storage (battery bank). The model is applied to the new part of a district energy system located in Switzerland. The best integrated district energy systems are presented as a set of Pareto optimal solutions by minimizing both the total annualized cost and equivalent CO2 emission while ensuring the reliable system operation to cover the demand. The results indicated that selection of the proposed optimal district energy system along with the storage brings great economic and environmental benefits in comparison to all other scenarios (conventional energy system, stand-alone system, and net zero-energy without storage)

Assessing the economic and energy efficiency for multi-energy virtual power plants in regulated markets: a case study in Egypt

This paper investigates the design and operation management of VPPs in regulated markets. A new framework based on profit maximization objective function is presented in this study. The hypotheses of this research is that considering profit as an objective function would yield a more realistic and optimal sizes compared to Cost of Energy (COE) minimization approach adopted in literature. The analyzed VPP aggregates solar PV units, CCHP supplying power and thermal energy, Battery storage system and thermal energy storage system. The system is formulated in an optimization model fed by energy demand profile, prices and inputs for solar power (irradiance and weather data). The objective function is formulated based on maximization of profit of the VPP selling power to the grid by Power Purchase Agreement (PPA), selling power to consumers at the public electricity tariff, and selling thermal energy at an assumed constant tariff. CCHP non-linear part-load efficiency is also considered in the model, accordingly, Genetic Algorithm (GA) is employed to solve the optimization. Results of the optimally configured model achieved 36% improvement in COE compared to literature. Solar power contributed by 31% from the total produced energy without imbalance, grid power contributed by 4%, and CO2 emissions reduced by 47% compared to full dependency on the grid. Statistical relationships were drawn showing the relationship between profit, energy and exergy efficiencies versus different CCHP capacities. In addition, analysis is provided for the efficiencies’ relation with the dumped heat from the CCHP

Development of an Optimization Model for Design and Planning of a Decentralized District Energy System

This dissertation reports the development of a optimization model to help designing a tri-generation system for a given newly-built district with its consumers to satisfy the heating, cooling, and hot water demands featuring 4th generation district energy characteristics. The aim is to find the best way to select the equipment among various candidates (capacities), the pipeline network among the buildings, and their electrical grid connections. The objective function includes the annualized overall capital and operation costs for the district along with the benefits of selling electricity to the grid. The distributed energy supply consists of heating, cooling, and power networks, different CHP technologies, solar array, chillers, auxiliary boilers, and thermal and electrical storage. The performance of the model was evaluated for designing two different case under various scenarios: (i) a combined heat and power design, and (ii) a combined cooling and power design both carried out for the new part of Suurstoffi district situated in Risch Rotkreuz, Switzerland with seven residential and office complexes. For the combined heat and power design, the scenarios are defined based on the existence or non-existence of the distribution network (both heat and electricity) and the effectiveness of the storage systems. Allowing heat exchange among the buildings leads to 25% reduction in the total annualized cost and 5% reduction in emission compared to the conventional districts. Simultaneous heat and electricity exchange results in a higher reduction equal to 40% of the base scenario. Adding storage systems opens up an opportunity to lower both costs and emission even more and turns the district to a net-zero energy and energy plus districts. For the combined cooling and power design, the effectiveness of the network is analyzed together with the potential of feeding absorption chillers using the heat from the solar and non-solar energy sources. More than 67% of CO2 emission reduction is achieved through the hybrid heat and solar-driven arrangement

IEA ECES Annex 31 Final Report - Energy Storage with Energy Efficient Buildings and Districts: Optimization and Automation

At present, the energy requirements in buildings are majorly met from non-renewable sources where the contribution of renewable sources is still in its initial stage. Meeting the peak energy demand by non-renewable energy sources is highly expensive for the utility companies and it critically influences the environment through GHG emissions. In addition, renewable energy sources are inherently intermittent in nature. Therefore, to make both renewable and nonrenewable energy sources more efficient in building/district applications, they should be integrated with energy storage systems. Nevertheless, determination of the optimal operation and integration of energy storage with buildings/districts are not straightforward. The real strength of integrating energy storage technologies with buildings/districts is stalled by the high computational demand (or even lack of) tools and optimization techniques. Annex 31 aims to resolve this gap by critically addressing the challenges in integrating energy storage systems in buildings/districts from the perspective of design, development of simplified modeling tools and optimization techniques

Energy Prediction and Optimization of the Hybrid Community District Heating System (H-CDHS)

The ever-increasing demand for energy in different sectors, such as building sector as one of the main consumers of the energy, is a result of a considerable surge in the world population, starting since the beginning of the industrial revolution in the late 18th century until the present. One of the direct consequence of this rapid growth was the overuse of fossil fuels as the world's main energy source resulting in a rapid depletion of them and thereby increasing the level of CO2 equivalent emissions at an atmospheric level known as greenhouse gasses. Increasing the concentration of these gasses at atmospheric level, exceeding the 400 PPM level for the first time in history, puts the earth at the point of no return. In order to sustain the economic growth while reducing the greenhouse gas concentration at an atmospheric level at the current stage, providing a clean sustainable solution which allows for a steady flow of energy is one of the most vital challenges facing the politician and energy planners. One of the solutions proposed by the energy planners which touches the higher level of energy management is to promote the usage of District Heating Systems (DHS). While designing an efficient DHS is highly dependant on accurate modeling of the thermal performance of the buildings, district users; yet, limited simulation tools capable of modeling the district energy systems, at a larger scale with a numerous user’s types and with an appropriated level of precision which can potentially be a very laborious and time-consuming process, have been developed. Besides many associated limitations, providing a realistic demand profile of the district energy systems is not a straightforward task due to a high number of parameters involved in predicting a detailed demand profile. To this end, this dissertation focuses on the development of the procedure for energy modeling and optimization of the Hybrid Community District Heating System (H-CDHS) with integrated centralized thermal storage, the 4th generation of district heating systems. To do so, this study describes the procedure used to develop two types of simplified models to predict the thermal load of a variety of buildings (residential, office, attached, detached, etc.). The predictions were also compared with those made by the detailed simulation models. The simplified model was then utilized to predict the energy demand of a variety of district types (residential, commercial or mix), and its prediction accuracy was compared with those made by detailed model: A good agreement was observed between the results. In next step, the proposed procedure was utilized to predict the heating demand profile of an existing community, WWH community in Glasgow. High prediction accuracy and low computational time of the proposed method illustrates the potential of the proposed method in predicting the heating demand profile of larger scale communities. In the last step, the proposed load prediction method was coupled with energy simulation tool (TRNSYS) and optimization tool (MATLAB/Simulink) in order to develop a simplified methodology for dynamic optimization of a hybrid community-district heating system (H-CDHS) integrated with a thermal energy storage system. Two existing and newly built community have been defined and the results of the optimization on the equipment size of both communities have been studied. The results for the newly build community is then compared with the one obtained from the conventional equipment sizing methods as well as static optimization methods to obtain potential reduction in equipment size using the proposed method

The design and operation optimization of liquid air energy storage within multi-vector energy systems

Climate changes call for the construction of a net-zero-carbon energy system across the globe. Such a massive need become more urgent due to the recent war on Ukraine, which has led to energy poverty, sharp rise in living costs and economic challenges particularly in Europe. Renewable energy represents a critical pathway towards the decarbonisation. A high share of renewable could trigger multiple problems due to the intrinsic intermittency and variability. Energy storage technologies offer the major solution to resolve such problems. There are many energy storage technologies at different development stages; among which, Liquid Air Energy Storage (LAES) is considered as a promising large-scale energy storage technology. The key advantages of the LAES include high scalability, no geographical constraints, cost-effectiveness, and capability of providing multi-vector energy services, which is expected to play an increasingly crucial role in future energy systems with a high renewable penetration. However, there are few studies working on the optimization and discussing the functions and benefits of LAES when it is applied into net-zero carbon energy systems. This forms the main motivation of this Ph.D. work, to address the research gaps. In the first and second parts of the thesis, the thermo-economic and dynamic simulation and optimization of the LAES system were conducted, which can provide the basis for discussing its key roles in distributed and grid-scale multi-vector energy systems. The given results can provide evidence for the optimal design, operation and improvement of LAES integrated systems. Meantime, the outcome can provide the enlightening views on the business investment decisions, and on developing renewable energy policies and storage expansion plans, to help achieve carbon mitigation ambitions in the UK by 2050. The following is a brief summary of the work and major conclusions: In the first part of this work, the multi-objective thermo-economic optimization of a stand-alone LAES system by using a Genetic algorithm was conducted, taking the round-trip efficiency (RTE) and economic indicators as the optimization objectives. The optimization has lead to a 9%~14% of increase in energy efficiency and a 14% of decrease in exergy destruction. The optimal design and operational parameters of LAES under different configurations and scenarios can be determined, including the optimal charging and discharging pressure, heat transfer areas, and mass flow rates of hot and cold storage media etc. Meantime, the design and operational guidelines of LAES can be derived. A LAES system with lower machine efficiencies requires lower charging and discharging pressure, while a system with worse heat transfer performance needs higher charging pressure but lower discharging pressure. Finally, the Pareto Front of capital costs, efficiencies and the occupied space energy density (OSDE) was obtained to provide system operators good investment advice of LAES. It indicated that a higher capital cost lead to a higher RTE, NPV and OSDE. Specifically, when the RTE increases by 1%, the optimized capital investment increases by 0.5-1%. If the investment budget is over 48 M£, a LAES system with three-stage compressors and four-stage turbines can produce better RTE than three-stage and four-stage LAES systems. In the second part of this work, the dynamic simulation and analysis of the LAES discharging unit were conducted to investigate its dynamic characteristic and response time when integrated with wind power. The results revealed that the LAES discharging unit is more suitable for responding to the wind power component at a time scale more than its start-up time, which can help compensate the wind power deficiency and reduce the motor fatigue. Meanwhile, the combined storage scheme with LAES and battery was proposed to smooth the varying wind power. The economic comparison among different storage schemes indicated the suitable storage system for wind power integration. The annual cost of solely battery storage is more than two times higher than that of the combined LAES and battery storage system, meantime, the larger the wind farm, the more obvious the economic advantages of the combined storage system. In the third part of this work, the multiple functions of LAES in decarbonizing a hybrid renewable micro-grid with high share of wind power were investigated. A mixed-integer linear programming (MILP)-based system design framework with the decoupled model of LAES was developed, which can determine the optimal sizes and operation of the micro-grid components and the LAES units. Specifically, the optimal charge/discharge energy to power ratio (27/14 h) and the storage tank size (608 t) of LAES in a micro-grid with 75% of wind power were obtained, leading to ~60% of carbon emission reduction on the 2016 level. The results also revealed the key roles of LAES in supporting a micro-grid with high share of wind power by providing multiple functions. The total benefits were split into six explicit revenue streams for the first time, including the time shifting (13.2%), renewable firming (11.4%), peak shaving (28%), flexibility (21%) and reserve value (20.4%), as well as the waste heat recovery (6%). It also indicated that a higher renewable percentage (over 50%) would be the major driving force to increase the attractiveness of LAES in micro-grids than the mildly reduced LAES capital cost and the enlarged electricity price differences. In the fourth part of this work, the cost-effective pathways and the storage needs for the transition to a net-zero carbon energy system in the UK by 2050 were assessed. A MILP-based energy expansion model was developed to achieve the optimal design and operation of the system. Firstly, the results revealed that a future 100% renewable or net-zero carbon power system is feasible with levelised cost of energy (LCOE) at 65~80 £/MWh, and a net-zero carbon heat system is affordable with the levelised cost of heat (LCOH) at 45~63 £/MWh. The major expansions are onshore wind power (94.5 GW) in power sector and air-source heat pump (~80 - 90 GW) in heat sector. Secondly, storage technologies would play crucial roles in a net-zero carbon system, only ~10-12% of investments in electric storages would reduce the total annual costs by ~15.1% - 28%. The major storage expansions lie in LAES (384 GWh) in power sector and the short-term heat storage (330 GWh) in heat sector. Thirdly, the newly deployed capacities of renewables and storages in different zones are correlated with each other, the LAES and renewable capacity ratio is around 20%. It also indicated that the LAES with the charge durations at 8~10 h and discharge durations at 14~15 h is more suitable for the wind-dominated case in the UK than short-duration batteries (~4/5h)