Design Of Distributed Energy Systems Research Articles

Complementarity reformulations for the optimal design of distributed energy systems with multiphase optimal power flow

The optimal design of grid-connected distributed energy systems (DES) has been previously investigated, but most studies exclude nonlinear multiphase optimal power flow (MOPF) constraints which capture imbalances in alternating current (AC) distribution networks. Previous DES design studies considering AC power flow assume balanced loads and networks, which is an assumption that does not hold for distribution networks that are often inherently unbalanced. This is the first study to present an optimisation framework for designing grid-connected DES while simultaneously considering multiphase optimal power flow (MOPF) constraints to accurately represent unbalanced low-voltage distribution. This study proposes a new algorithm for obtaining DES designs subject to nonlinear multiphase power flow constraints with regularised complementarity reformulations for operational constraints containing binary variables. DES design models with either MOPF or OPF are tested using an unbalanced distribution network, where rooftop solar, gas boilers, and battery storage are considered as distributed resources. Despite the increased complexity, DES design with MOPF obtains the best solution, with 81% greater solar capacity and 6% lower total annualised cost when compared to DES with OPF. Designs from a commonly-used linear DES design framework based on direct current (DC) approximations produce the highest costs (8% more than initially predicted) when tested with MOPF. The proposed algorithm with complementarity reformulations achieves a 19% improvement in the objective value over a previous two-stage decomposition for DES with OPF, where the entire binary topology in the nonlinear model is fixed. The study therefore enables the acquisition of DES designs that can work symbiotically within unbalanced distribution networks.

Discrete optimal designs for distributed energy systems with nonconvex multiphase optimal power flow

The optimal selection, sizing, and location of small-scale technologies within a grid-connected distributed energy system (DES) can contribute to reducing carbon emissions, consumer costs, and network imbalances. There is a significant lack of studies on how DES designs, especially those with electrified heating systems, impact unbalanced low-voltage distribution networks to which most DES are connected. This is the first study to present an optimisation framework for obtaining discrete technology sizing and selection for grid-connected DES design, while simultaneously considering multiphase optimal power flow (MOPF) constraints to accurately represent unbalanced low-voltage distribution networks. An algorithm is developed to solve the resulting Mixed-Integer Nonlinear Programming (MINLP) formulation. It employs a decomposition based on Mixed-Integer Linear Programming (MILP) and Nonlinear Programming (NLP) and uses integer cuts and complementarity reformulations to obtain discrete designs that are also feasible with respect to the network constraints. A heuristic modification to the original algorithm is also proposed to improve computational speed. Improved formulations for selecting feasible combinations of air source heat pumps (ASHPs) and hot water storage tanks are also presented. Two networks of varying size are used to test the optimisation methods. Designs with electrified heating (ASHPs and tanks) are compared to those with conventional gas boilers. The algorithms outperform one of the existing state-of-the-art commercial deterministic MINLP solvers, which fails to find any solutions in two instances within specified time limits. While feasible solutions were obtained for all cases, convergence was not achieved for all, especially for those involving the larger network. Where converged, the algorithm with the heuristic modification has achieved results up to 70% faster than the original algorithm. Results for case studies suggest that including ASHPs can support up to 16% higher renewable generation capacity compared to gas boilers, albeit with higher ASHP investment costs, as local generation and consumption minimises network violations associated with excess power export. The results also show the importance of including nonlinear power flow constraints in DES design problems. The optimisation framework and results can be used to inform stakeholders such as policymakers and network operators, to increase renewable energy capacity and aid the decarbonisation of domestic heating systems.

Flexibility index for a distributed energy system design optimization

Flexibility plays a critical role in the design of distributed energy systems (DESs) as it encompasses various aspects related to demand, storage, and supply. To optimize the capacity configuration of DESs effectively, a novel flexibility index (FI) is proposed in this study. The FI is constructed based on the fuzzy best-worst method with considerations for economy, autonomy, energy efficiency, and environmental friendliness, aligning with the characteristics of the DES. Considering FI, particle swarm optimization is employed to determine the optimal design scheme for the DES. A case study involving a swimming pool in Changsha City is conducted to demonstrate the reliability of the proposed optimization scheme. Furthermore, a multi-objective optimization model based on non-dominated sorting genetic algorithm-II and technique of ordering preferences for ideal solution similarity algorithms is developed with different objective functions. The results show that the system optimized considering flexibility maintains better performance, with system flexibility, renewable energy penetration, and off-grid degree indices of 0.824, 0.780, and 0.757 respectively. In addition, the optimization of system configuration considering flexibility can dynamically respond to diverse energy demands, maintaining lower operation and maintenance costs ($11223.75 per year), and lower CO2 emissions (91800kgCO2 per year). The quantified FI presented in this study provides a user-friendly and reliable optimization index for the design of DESs.

A framework for quantifying the value of information to mitigate risk in the optimal design of distributed energy systems under uncertainty

Distributed energy systems (DESs) are regarded as promising systems for integrating renewable energy sources. However, uncertainties arising from renewable energy and loads introduce significant complexity to DES design and may even result in reliability and economic risks when the design of DESs relies on limited information. Gathering more information can reduce uncertainty, thereby improving the robustness of the DES scheme. However, obtaining information comes at a cost, and too much information can result in redundant work and unnecessary computing burden. Conversely, discarding or ignoring information may pose risks to reliability and the economy. Therefore, this study presents a framework for quantifying the value of uncertainty information, which can help to understand how information affects risk and identify key information that facilitates DES risk aversion. Two information value indices, namely the expected values of information for reliability (EVPIr) and economy (EVPIe), are developed to measure the risk reduction of reliability and economy when more information is added to the design of DESs. Furthermore, a two-layer information value quantification model based on mixed integer linear programming is built to optimize the design of DESs based on uncertain information and quantify the value of information based on a relatively complete information set. The proposed information value quantification method is tested on a real DES under three types of uncertain design boundary scenarios. The results show that the values of EVPIr and EVPIe decrease with increasing information of uncertain design boundary scenarios, indicating that more information reduces risks. An unexpected discovery is that the probability information of the scenario set is not critical for DESs. The deviations of EVPIe are within ±2%. The proposed approach offers a quantitative means to evaluate and filter key information for planning scenarios, which can facilitate the generation of streamlined planning scenarios without compromising reliability and economy.

Optimal design of energy-flexible distributed energy systems and the impacts of energy storage specifications under evolving time-of-use tariff in cooling-dominated regions

To utilize the energy flexibility of demand side participants for achieving the global carbon-neutrality target, distributed energy systems (DESs) with active energy storages have attracted increasing attention. Existing studies mainly address the optimal configuration of DESs for enhanced energy flexibility. But the impacts of the energy storage specifications for DESs are rarely investigated, particularly under the evolving electricity markets towards carbon neutrality. This study, therefore, investigates the optimal design of energy-flexible DESs in cooling-dominated regions and the impacts of the economic and technical parameters of active energy storages under the evolving ToU tariff. A two-stage optimization method is developed for the optimal design of energy-flexible DESs. A district in Hong Kong is chosen as a reference site to perform the design case studies and impact analysis. It is found that the ToU tariff has significant impacts on the optimal design of energy-flexible DESs. The increase of the peak-to-valley ratio generally can increase the life-cycle economic benefits of DES energy-flexibility investment. The annualized life cycle cost of the optimized energy-flexible DES is decreased by up to $ 0.31 million when the peak-to-valley ratio increases from 2 to 10. A high unit price of energy storages (i.e., higher than 210 $/kWh for electric energy storages and 60 $/kWh for cold energy storages) will make them not economical to be adopted under all ToU tariffs. The maximum discharging rate of cold storages and the four specification parameters (i.e., the maximum charging rate, maximum discharging rate, charging/discharging efficiency, and lower limit of state of charge) of electric energy storages have considerable impacts on the energy-flexible DES design, and therefore are recommended to be optimized in design.

Global sensitivity analysis for design and operation of distributed energy systems: A two-stage approach

Distributed Energy Systems (DES) can play a vital role as the energy sector faces unprecedented changes to reduce carbon emissions by increasing renewable and low-carbon energy generation. However, current operational DES models do not adequately reflect the influence of uncertain inputs on operational outputs, resulting in poor planning and performance. This paper details a methodology to analyse the effects of uncertain model inputs on the primary output, the total daily cost, of an operational model of a DES. Global Sensitivity Analysis (GSA) is used to quantify these effects, both individually and through interactions, on the variability of the output. A Mixed-Integer Linear Programming model for the DES design is presented, followed by the operational model, which incorporates Rolling Horizon Model Predictive Control. A subset of model inputs, which include electricity and heating demand, and solar irradiance, is treated as uncertain using data from a case study. Results show reductions of minimum 25% in the total annualised cost compared to a traditional design that purchases electricity from the centralised grid and meets heating demand using boilers. In terms of carbon emissions, the savings are much smaller, although the dependency on the national grid is drastically reduced. Limitations and suggestions for improving the overall DES design and operation are also discussed in detail, highlighting the importance of incorporating GSA into the DES framework.

Global sensitivity analysis and stochastic optimization of multi-energy complementary distributed energy system considering multiple uncertainties

Multiple uncertainties exist in the planning and design of multi-energy complementary distributed energy system (DES). In order to solve this problem, a more complete framework of two-stage stochastic programming (TSP) approach for optimal multi-energy complementary DES planning and design under multiple uncertainties is proposed. The developed TSP model is formulated as a mixed integer linear programming, which is intended to minimize the equivalent annual cost (EAC) of the multi-energy complementary DES. Probability scenarios of key uncertain parameters selected by treed Gaussian process method for the TSP model are generated and reduced using discrete approximations of probability distributions method and random vector sampling method. The developed method framework is applied to a case study high-rise office building in planning stage to illustrate the TSP model's output. And multiple evaluation indicators including configuration, economy, energy and environment are used to compare and analyze the optimal schemes obtained by deterministic model and TSP model under different building load scenarios. The results show that of the 26 uncertain parameters considered only 7 are selected as the key parameters for the TSP model in this paper. EAC of the optimal multi-energy complementary DES may be overestimated if without considering key uncertain parameters. Thus, the deterministic optimization practices and the cost estimates resulting from them can be considered unreliable. Moreover, the multi-energy complementary DES would want to increase CO2 emission to achieve better economic performance. Finally, the energy share distribution of all scenarios in the stochastic model provides more reliable and more accurate information for decision-makers.

Improved Structural Local Thermal Energy Planning Based on Prosumer Profile: Part B

Distributed energy systems (DES) are currently at the forefront of the energy transition. Their placement brings production closer to the demand side of urban and sub-urban environments, making optimal design a necessity. However, the complexity of accurately addressing the energy demands via DES has received increasing research attention. This is mainly due to the impact they have on the energy transition’s socioeconomic aspect, as these systems are far from viable in most cases, especially when cutting-edge renewable technologies are involved. The current study aims to provide a practical and non-repetitive approach to DES design, explicitly referring to thermal distributed supply systems (TDESS). The authors present the last two of their three-layer Hierarchically Dependent Layering Methodology (HDLM) approach in designing a thermal local energy community (TLEC) from the ground up. The 2nd layer is the superstructure design of the TLEC, where a map approach is introduced and explores several combinations of the selected equipment, how they will interact to meet the heating and cooling loads and how they will form the superstructure. The 3rd is the economic assessment of the proposed scenario. The study results indicate relative ease of use of the model, as a non a priori approach is needed. Additionally, the proposed solution is economically viable as the respective performance indicators suggest.

On the interaction between the design and operation under uncertainties of a simple distributed energy system

PurposeThe purpose of this study is toward a better understanding of the interplay between the design and the operation under uncertainties of a simple distributed energy system (DES), by analyzing the sensitivity of the operation strategy over the size of the assets.Design/methodology/approachA two-step framework is developed in this work: first, the equipment sizes are obtained solving an integrated design approach where an operation strategy Fd is embedded in a design loop; then, once the sizes have been fixed, the DES is evaluated with an operation strategy Fa (which can be the same as Fd). The operation strategies Fd and Fa are not necessarily the same, so the objective of the paper is to study the interplay between the design and the operation by varying the optimality level of the operation strategies in both phases.FindingsThe results show that the design of DES cannot be approached without considering its close relationship with the operation strategy. Indeed, the design method needs to be chosen according to the performance of the operation policy finally used in real life: no matter if the operation strategies are the same in both phases but they must lead to a similar level of optimality in terms of operating performance.Originality/valueThe originality of this work is to shed light on the importance of the operation strategy in the design procedure as it seems rarely addressed, to the best of the authors’ knowledge, in the literature. Indeed, most of the paper dealing with stochastic design of DES solves single large two-stage problems without discussing the way power flows are finally controlled in real life. The optimal design and operation of DES is rarely addressed conjointly, this study aims at bridging the gap between these two isolated scientific communities.

Performance evaluation of distributed energy system integrating photovoltaic, ground source heat pump, and natural gas-based CCHP

A distributed energy system (DES) is considered energy-efficient and eco-friendly. However, with increasing renewable energy penetration into energy supply systems, the DES design has become diversified. This study aims to scrutinize the DES performance considering DES design characteristics and renewable energy penetration. Two DESs consisting of photovoltaic, ground source heat pump, and natural gas-based combined cooling, heating, and power with different operation strategies are modeled by TRNSYS. In DES1, the building cooling demand is first met by absorption chillers, while in DES2, it is first met by the ground source heat pump. The energy, environmental, and economic performances under different photovoltaic electricity supplies are analyzed with a cultural industrial park selected for a case study. The sensitivity analysis considering energy price and renewable electricity penetration on the system performance is also performed. The results reveal that the primary energy saving ratio and comprehensive emission reduction ratio of DES1 are respectively 5.52–13.47% and 36.76–54.09%, while those of DES2 are respectively 16.75–26.83% and 16.29–26.71%. Besides, the total cost saving ratio is 0.29–4.79% for DES1 and −14.27– -10.77% for DES2, respectively. The sensitivity analysis indicates that the cost saving ratio of the two DESs rises as the grid electricity price increases. In contrast, their trends are opposite as the natural gas price increases. The renewable electricity penetration has a greater effect on DES1 performance than on DES2 performance. This study can facilitate the better design and performance evaluation of DESs.

Multi-objective optimization design and multi-attribute decision-making method of a distributed energy system based on nearly zero-energy community load forecasting

Currently, many scholars have researched distributed energy systems (DES) from different dimensions. However, the load forecasting on both source-side and load-side, the optimal design of DES, and the multi-attribute decision making need further research. Therefore, a novel DES combining solar photovoltaic and hybrid energy storage is proposed. Combining the Monte Carlo method and improved K-means clustering is presented to predict the load on the source-side and load-side. Then, an optimal design method considering system independence and solar energy utilization scale is proposed, and the novel system is optimized. The entropy method and TOPSIS method are combined to make the multi-attribute decision on the optimization results of the system. The novel system is used to supply energy to nearly zero-energy communities (NZEC) in different scenarios. The research results show that the proposed load forecasting method can more accurately reflect the actual load demand of users. When “cost - external energy dependence - solar fraction” is taken as the target, the comprehensive energy import rate with nearly zero-energy office communities is only 36.7%, showing excellent independence. Finally, this paper can provide a method for load forecasting of NZECs, and provide certain theories for the optimization and decision-making of DESs.

Impact of carbon pricing on distributed energy systems planning

Carbon pricing is being implemented by several governments to curb CO2 emissions. This work studies its impact on distributed energy systems design which are powered by both renewable and fossil fuels. In particular, the analyses investigate a real case study situated in Singapore, characterized by cooling and electricity demands.The goal of the analyses is to determine whether carbon pricing does impact design choices in meeting the energy demands of a user located in a cooling dominated region, and secondly in assessing the effectiveness of carbon pricing as a CO2 emissions mitigation policy. This is achieved by investigating the optimal design of the energy systems meeting the demands of the test case under different carbon pricing and primary energy supply costs assumptions.The results indicate that the optimal design under current conditions heavily relies on PV with more than 10 MWp worth of capacity and that an increasing carbon pricing would lead to a lower environmental footprint. But if the supply costs for natural gas were to lower, the optimal design would switch to relying on a combined cooling heat and power-based electricity generation system up to 3 MWe, increasing the primary energy consumptions regardless of the carbon pricing scheme in place. This would also happen even at significantly higher prices than the scheme under evaluation.

Dynamic aware aging design of a simple distributed energy system: A comparative approach with single stage design strategies

This paper focuses on the integrated management and design of a distributed energy systems (DES) with solar generation and energy storage. The DES remains voluntary simple as the objective is to focus on the design methodologies rather than the system complexity. The article aims at bridging the gap between conventional DES design strategies, made in a single stage fashion over a representative period, and expansion planning problems that perform dynamic sizing over decades with oversimplifications of the system operations. Especially, the paper investigates to what extent the value of the model is increased when aging is controlled over the system lifetime compared to standard methods based on a single equivalent year. To address these questions, a multi-time scale model is first implemented by coupling both the DES operation and the sizing. The optimal asset capacities are computed in the form of a dynamic investment plan over the system lifetime that can accommodate potential changes in energy prices or cost of technology. Then, the results are compared with single stage design strategies on a common simulation framework. The implemented multi-time scale planning displays good performances with up to 20% cost reduction compared to typical single stage designs. Finally, the impact of the energy rates and system self-sufficiency are investigated. The obtained results show that significant investments in energy storage arise for electricity prices multiplied by three compared to the baseline or with strong self-sufficiency constraint over 60%.

The coupling impact of subsystem interconnection and demand response on the distributed energy systems: A case study of the composite community in China

Distributed energy systems (DES) with hybrid sources has become a more effective way to increase the flexibility of energy supply. Current relevant research on DES design optimization strives to improve system efficiency and sustainability through independent application of subsystem interconnection and demand response. This paper aims to analyze the coupling impact of the above two strategies on the performance of DES. A general multi-objective mathematical programming model is proposed to work out the optimal design and operation scheme of DES, as well as flexible management of electricity, cooling energy, heating energy and steam. The minimum annual cost, carbon emission and energy demand deviation are set as objective functions. Additionally, the augmented ε-constraint method is introduced to search for the trade-off among three indicators. A composite community in China is taken as a case and three scenarios are carried out for comparison. Results reveal that the annual cost and carbon emission are further decreased by 7.3% and 32.1% under the coupling impact of subsystem interconnection and demand response. Eventually, the preferred strategy is analyzed in detailed to illustrate how this coupling strategy further improves the performance of DES.

Capacity design of a distributed energy system based on integrated optimization and operation strategy of exergy loss reduction

With the increase in the energy demands for heating, cooling, and electricity for buildings, the distributed energy system (DES), which is driven by renewable energy and natural gas, has become an economic and environmentally beneficial option for energy generation and supply. This paper presents a method for the integrated optimization of component capacity and annual operation of DES. In the outer cycle optimization, the capacity of energy generators and energy storages is optimized together to match the fluctuating energy demands of the whole year. In the inner cycle optimization, the hourly operation of energy generators and energy storage is managed following the operation strategy that reduces the exergy loss rate.Case studies of DES design under different grid connection modes for an office building demonstrate the feasibility and flexibility of the proposed method. A traditional centralized system is employed as a baseline of performance, and two other capacity design methods are utilized for comparison. Results show that DES designed by the proposed method reduces carbon emissions by 0.170 kg/kWh, energy consumption by 0.392 kWh/kWh, and operation costs by 0.123 CNY/kWh, on average. In contrast to DESs designed by other methods, the performance of these parameters is improved by 0.019 kg/kWh, 0.078 kWh/kWh, and 0.031 CNY/kWh, respectively. Moreover, the DES design exhibits advantages in exergy saving, uncertainty resistance, and power grid independence.

Three-stage optimization method for distributed energy system design under uncertainty

Reasonable capacity configurations of distributed energy system are issues which need to be discussed. Determinate design without considering variations in energy load and energy prices can result in non-achievement of project targets during its service life. Therefore, a design method that takes into account uncertain factors takes precedence over other methods. In this paper, a three-stage optimization method is proposed to provide theoretical guidance on the optimization of combined cooling, heating, and power system configurations. The first two stages link the optimization of the operation strategy and equipment capacities simultaneously under current load and energy prices. The Monte-Carlo simulation is applied in the third stage to fully consider the effects of various possible scenarios, and the Tabu search algorithm was introduced for system optimization. The comprehensive benefits include energy consumption, economy, and emission level. These were taken into consideration in the objective function. Moreover, a detailed design process was presented to illustrate the application of the proposed method. In conclusion, the proposed method is not only suitable for the design of combined cooling, heating, and power system, but could easily extend to other energy system easily.

Emerging supply chain of utilising electrical vehicle retired batteries in distributed energy systems

Increasing electric vehicles (EV) penetration leads to significant challenges in EV battery disposal. Reusing retired batteries in distributed energy systems (DES) offers resource-circular solutions. We propose an optimisation framework to model the emerging supply chains and design strategies for reusing the retired EV batteries in DES. Coupling a supply chain profit-allocation model with a DES design optimisation model, the framework maximises the whole chain profit and enables fair profit distribution between three interactive sectors, i.e., EV, DES, dismantling and recycle (D&R) sectors. Our research highlights the system implications of retired batteries on DES design and new modelling insights into incentive policy effectiveness. Our case study suggests significant potential value chain profits (2.65 million US$) achieved by deploying 10.7 MWh of retired batteries in the DES application with optimal retired battery price of 138 US$/kWh. The revenue support on D&R sector is suggested as a promising incentive scheme than tariff support.

Redefining energy system flexibility for distributed energy system design

A novel method is introduced in this study to consider flexibility taking into account both system design and operation strategy by using fuzzy logic. A stochastic optimization algorithm is introduced to optimize the system design and operation strategy of the energy system while considering the flexibility. GPU (Graphics Processing Unit)-accelerated computing is introduced to speed up the computation process when computing the expected values of the objective functions considering a pool up to 5832 scenarios. Subsequently, a Pareto optimization is conducted considering Net Present Value (NPV), Grid Integration (GI) level (which represents the autonomy level of the energy system) and system flexibility. The case study assesses an energy system design problem for the city of Lund in Sweden. According to the obtained NPV and GI Pareto front, a renewable energy penetration level covering more than 45% of the annual demand of the energy hub (an integrated energy system consisting of wind turbines, solar PV panels, internal combustion generator and a battery bank) can be achieved. However, the flexibility of the system notably decreases when the renewable energy penetration level exceeds above 30%. Furthermore, the results show that poor system flexibility notably increases the risk of higher-loss of load probability and operation cost. It is also shown that the utility grid acts as a virtual storage when integrating renewable energy sources. In this context, a grid dependency level of 25–30% (of the annual energy demand) is sufficient while reaching a renewable energy penetration level of 30% and maintaining the system flexibility.

Optimal design of distributed energy systems for industrial parks under gas shortage based on augmented ε-constraint method

Natural gas distributed energy systems have developed rapidly owing to their high efficiency, low environmental impact, high energy supply reliability, and good economic returns. As the main users of natural gas distributed energy, industrial parks account for 67.7% of the total installed capacity of the industry. Therefore, disrupted gas supply to industrial parks during gas shortage periods results in decreased production and consequently huge economic losses. This study addresses this issue by attempting to develop an optimized design for distributed energy systems and natural gas allocation in multiple industrial parks under gas shortage conditions. A multi-objective mathematical programming (MOMP) model is established to minimize the total construction investment and operation costs of each industrial park considering emission, energy balance and other technical constraints. Using the augmented ε-constraint method, optimal configurations of distributed energy systems, operation strategy, and economic and emission performance of each industrial park are determined. The distributed energy system design for three industrial parks in Jinan, China, is taken as an example to verify the model. Compared with the non-cooperation case, the annual total cost of full-cooperation can be reduced by 8.29%, and the annual total carbon emission of full-cooperation can be reduced by 427.55 t. Additionally, sensitivity analyses of energy price and total input natural gas are conducted to provide further insights into the optimization of distributed energy system for each industrial park. The results show that electricity price has the strongest impact on the economic performance of the industrial parks, followed by natural gas price and biomass price, but natural gas price does not affect the gas allocation ratio. Moreover, when the total input natural gas is increased by 10–90%, the total cost can be reduced by 0.69%–7.83%.

Optimal design of distributed energy systems in rural area of developing country: a case study of Guanzhong area, China

This paper optimized the design of renewable distributed energy systems (R-DES) based on interval linear programming. The total cost of this system is used as an objective function of system configuration problem. The optimal configuration of this system is obtained. In order to prove practicability of this system, this paper took the remote rural areas of Guanzhong as an example to optimize energy supply and operation mode. Through calculations, it can be seen that this system reduced power supply by 29.0%~63.6%, and carbon emission reduction rate is 4.0%~49.6%.