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The design of decentralized energy conversion systems in smart residential microgrids is a challenging optimization problem due to the variety of available generation and storage devices. Common measures to reduce the problem's size and complexity are to reduce modeling accuracy, aggregate multiple loads or change the temporal resolution. However,...
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... model requires more electricity imports but simultaneously leads to increased electricity exports. This finding suggests that the compact model is able to slightly better coordinate generation and demand, since higher self-consumption more profitable than increased electricity imports and exports. Both models converge within 2178 and 1340 s. Fig. 4 displays the heat coverage ratios for both models and both simulations. With 3 small buildings, approx. 95% of the heat demand is generated through boilers in the distributed model and 91% in the compact model. The remaining heat is provided by STC. In the microgrid with large buildings, STC is not used, instead heat is generated by ...
Citations
... Mathematical optimization is an effective tool to design energy systems that are optimal, e.g., have minimal total annualized cost or global warming impact, and can be leveraged to design energy systems that are robust towards the volatility introduced by VRES ( Biegler and Grossmann, 2004;Yunt et al., 2008;Lubin et al., 2011;Li and Barton, 2015). Optimization has been successfully applied to design energy systems across various scales, from utility systems at the plant scale (Papoulias and Grossmann, 1983;Voll et al., 2013;Bahl et al., 2018; to energy systems for districts (Bünning et al., 2018;Schütz et al., 2018;Teichgraeber and Brandt, 2019) up to power systems on islands (Ma et al., 2014;Gils and Simon, 2017;Barone et al., 2021) and on the (inter)-national scale (Kannan and Turton, 2013;Siala et al., 2019;Reinert et al., 2020). For a review of modeling tools for renewable energy systems, we refer to Ringkjøb et al. (2018). ...
Time-series information needs to be incorporated into energy system optimization to account for the uncertainty of renewable energy sources. Typically, time-series aggregation methods are used to reduce historical data to a few representative scenarios but they may neglect extreme scenarios, which disproportionally drive the costs in energy system design. We propose the robust energy system design (RESD) approach based on semi-infinite programming and use an adaptive discretization-based algorithm to identify worst-case scenarios during optimization. The RESD approach can guarantee robust designs for problems with nonconvex operational behavior, which current methods cannot achieve. The RESD approach is demonstrated by designing an energy supply system for the island of La Palma. To improve computational performance, principal component analysis is used to reduce the dimensionality of the uncertainty space. The robustness and costs of the approximated problem with significantly reduced dimensionality approximate the full-dimensional solution closely. Even with strong dimensionality reduction, the RESD approach is computationally intense and thus limited to small problems.
... The second form of multi-stage optimization utilizes decomposition algorithms, notably the Dantzig-Wolfe decomposition [134]. This method entails an iterative process that includes several subproblems and a master problem. ...
This article comprehensively reviews strategies for optimal microgrid planning, focusing on integrating renewable energy sources. The study explores heuristic, mathematical, and hybrid methods for microgrid sizing and optimization-based energy management approaches, addressing the need for detailed energy planning and seamless integration between these stages. Key findings emphasize the importance of optimal sizing to minimize costs and reduce carbon dioxide (CO 2) emissions while ensuring system reliability. In a pedagogical manner, this review highlights the integrated methodologies that simultaneously address sizing and energy management and the potential of emerging technologies, such as smart grids and electric vehicles, to enhance energy efficiency and sustainability. This study outlines the importance of accurate load modeling and carefully selecting models for renewable energy sources and energy storage systems, including degradation models, to achieve long-term operational efficiency and sustainability in microgrid design and operation. Future research should focus on developing multi-objective optimization techniques and incorporating cutting-edge technologies for improved microgrid planning and operation.
... Reference [9] designed a CG based distributed computing framework for multi-stage stochastic planning of MGs. An iterative CG method was proposed in [10] for MG energy management, which successfully reduced the size of the problem. ...
In this paper, we apply a model predictive control based scheme to the energy management of networked microgrid, which is reformulated based on column generation. Although column generation is effective in alleviating the computational intractability of large-scale optimization problems, it still suffers from slow convergence issues, which hinders the direct real-time online implementation. To this end, we propose a graph neural network based framework to accelerate the convergence of the column generation model. The acceleration is achieved by selecting promising columns according to certain stabilization method of the dual variables that can be customized according to the characteristics of the microgrid. Moreover, a rigorous energy management method based on the graph neural network accelerated column generation model is developed, which is able to guarantee the optimality and feasibility of the dispatch results. The computational efficiency of the method is also very high, which is promising for real-time implementation. We conduct case studies to demonstrate the effectiveness of the proposed energy management method.
... While the decentralization of energy conversion systems is a well-published topic in Engineering Journals (for recent reviews, see [15][16][17]), the optimal dislocation of strategic industrial lines across a country is usually studied by economists and social scientists (for a review, see [18]). This section offers a limited and succinct description of both lines of investigation, the goal being here to underscore the multi-dimensionality of the problem and of its implications and the unavoidable failure to tackle the problem with purely monetary or purely socio-economic approaches. ...
In the ongoing debate about the feasibility of enforcing a transition to decentralized energy conversion systems, arguments are often presented that lack scientific rigor. Granted, the issue is multi-faceted and fundamentally multi-disciplinary, and possible solutions strongly depend on the selection of location as well as on local climate and demographics. Furthermore, decentralizing the final energy distribution leads to potential socio-economic considerations that involve value judgements. However, the most serious problem is that media have appropriated the topic and are often publishing opinion papers authored by non-specialists and even by representatives of interest groups. The present paper proposes an approach that is innovative on two counts: first, it treats “final energy” as any other commodity and therefore expands the field of investigation to the problems arising from the decentralization of a generic production line or technological chain; second, it argues that a method solidly rooted in Thermodynamics, the Extended Exergy Accounting, may be used to quantify the total amount of primary exergy resources requested by a decentralized strategy (as opposed to a centralized one), so that a comparison can be performed and discussed on a rational, unbiased and scientific basis. This is an introductory paper that reports some theoretical results of the method: realistic applications are perforce excluded because the idea is that the procedure must be drafted in such a way to be applicable to different socio-economic scenarios and locations and to remain valid under a broad range of boundary conditions.
... The transition towards decentralized energy systems, characterized by enhanced resilience and efficiency, is gaining momentum. Research by Harb et al. [12] and Schütz et al. [13] supports the decentralization of heating systems and energy conversion within microgrids, respectively. Middelhauve et al. [2] further discuss the optimization of community-level renewable energy hubs. ...
... π s c(i) = P(π c , d c ) (12) π s u(i) = P(π u , d u ) (13) s ∈ P (x i ), u ∈ U N IT S, c ∈ COST S ...
In the face of escalating climate concerns and the push for sustainable development, the global shift towards renewable and decentralized energy systems presents new challenges and opportunities. This study investigates integrating decentralized energy production, particularly photovoltaic (PV) systems, into national energy planning, aiming to optimize energy strategies that balance local production and consumption with national objectives. By analyzing the Swiss energy model, the research employs the EnergyScope and REHO models to assess the strategic implications of decentralized versus centralized energy systems. Results show that a decentralized approach can significantly reduce PV installation needs to 35 GW, about 23% of potential capacity, and decrease annual system costs by 10% to CHF 1230 per capita. This strategy emphasizes local consumption, minimizes grid reinforcement demands, and leverages economic advantages while addressing overproduction challenges through effective energy storage and grid management. Conclusions underline the strategic value of combining centralized and decentralized methods for resilient and sustainable energy planning. The study contributes to the discourse on energy policy and infrastructure planning, advocating for a hybrid model that accommodates both local conditions and broader energy objectives, urging further research into climate impacts and technology integration for a comprehensive energy future.
... Promoting LEMs among distribution networks is a strategic focus of global energy policies. Some examples can be found in the EU-funded pilot projects FLEXITRANSTORE [2] InterFlex [3], EMPOWER [4], and DREAM-GO [5]. ...
Local Electricity Markets (LEMs) arise as new layers of current market designs, enabling local trading of flexibility products. In many cases, LEMs encompass several networks, involving different Distribution System Operators (DSOs). This setting raises intrinsic concerns regarding the information privacy of the involved DSOs while trying to achieve system-wide efficiency. In this paper, a market design for the trading of flexibility products in inter-DSO LEMs is proposed where the DSOs are allowed to trade among the areas under the coordination of a Local Market Operator (LMO). A coordinated and decentralized approach based on the Alternating Direction Multiplier Method is proposed. The novel aspect of this work is that each DSO self-schedules its own assets in response to market signals (optimal dual variables) to fulfil the flexibility requests from itself or from other DSOs. An illustrative case study based on the IEEE 123 bus test systems is used for testing the proposed framework.
... However, the problem formulation does not consider installation or sizing decisions of the technologies. Schütz et al. [28] apply the Dantzig-Wolfe decomposition to a district energy system with a microgrid in which the BESs form the subproblems. The energy balances of the microgrid represent the master problem which optimizes the electricity exchange between buildings. ...
... First, the full master problem is solved for a limited amount of extreme points. With a solution of this reduced master problem, the dual variables are determined and new columns can be generated by solving the subproblems and finding an optimal solution for: [34,28]. Applied to a district with 5GDHC network and microgrid, the proposed decomposition approach provides shadow prices for each time step. ...
... -(28) as well as the storage constraints (29) -(32) of the full formulation are included in the master problem. The master problem also includes the thermal and electricity balance of the energy hub, i. e. constraints(33) and(34). ...
The adequate planning of district energy systems is a key process to provide communities with proper heating and cooling networks. To find cost-effective planning solutions, this decision-making process is usually formulated as mathematical model, which is optimized to determine where and how many assets should be installed in the network. However, this optimization problem is becoming more complex as district energy systems evolve and become more elaborate. For the 5th generation district heating and cooling (5GDHC) networks, this planning framework comprises multiple building energy systems, a thermal and electrical network as well as central heating and cooling units. As a result, the optimization problem associated with these circumstances can easily become intractable as the number of elements of the network increases. To alleviate this tractability problem, in this paper, a Dantzig–Wolfe approach is devised to decompose a mixed-integer linear program into multiple subproblems (for every building) and a master problem (thermal and electrical network and central units). A realistic case study based on a 5GDHC system in Germany is considered. For this case study, it is demonstrated that the proposed decomposition approach yields the same results attained by the original not decomposed problem while achieving significant gains in terms of scalability and computational times. More specifically, the decomposition approach reduces the computational time for districts with more than 10 buildings by in average 94%. This corroborates the potential of the proposed approach to improve the computational efficiency of models that will deliver cost-effective investment plans for 5GDHC networks.
... Typical 'energy systems' or 'energy hub' refer to small scale microgrids at a building or district scale [3], that may or may not include various energy vectors (e.g. electricity, heat, gas) such as in [4] [5] [6] in order to take advantage of the interactions between different equipment and leverage additional flexibility. From the mathematical point of view, the optimal management of those systems is typically represented as power dispatch problems with energy exchanged between the various sources and sinks, subject to balance constraint at one 'energy node'. ...
... For better accuracy, typical approaches in the literature account for the weight wk of each representative day c k * , computed as the ratio between the corresponding cluster size ||Dk|| and the original numbers of days ||D|| [6] This leads to a modification of the problem, discretized along the different representative days. Especially, the control variables are arranged in daily profiles u t d c k * and the constraints are defined along the set of representative days and daily time set Td. ...
The paper proposes a strategy for the time horizon reduction in power and energy studies. The method denoted Optimized Weighted Time Slices is compared with conventional approaches based on representative days that rely on unsupervised and supervised clustering as well as different strategies to reconstruct the problems. Those reference methods suffer from a lack of scalability when high numbers of dimensions are considered and their outputs strongly depend on the starting point in the partitioning process. The proposed strategy is based on a hierarchical clustering coupled with a least square minimization. The originality of the approach is that it works on individual time slices rather than on representative periods. Those representative time samples are furtherly optimized considering fitting criteria with the input time series thanks to a linearization of the duration curves. All the time modelling methods are tested on both a simple energy hub at a building scale (i.e. load, solar storage) and on a 33-buses distribution network with storage. The methods performances are assessed while comparing the results of the systems operation over the reduced time horizons with the outputs from full yearly simulations. In particular, complex objective functions are considered for the systems operation, as it is shown that they impact the accuracy of the time reduction as much as the systems complexity itself. The proposed strategy displays smaller errors (1%–5% more accuracy) than the reference methods, is much more scalable (>10 times faster), and systematically returns the same outputs.
... It overcomes the scalability issue of the ADMM [12] as well as the limitations of presented state-of-the-art methods in Table 1. In addition, the algorithm is straightforward to implement as the SPs require minor adaptations during the re-formulation in order to obtain the decomposed problem [15]. Historically the DWD has been designed for linear problems. ...
... Historically the DWD has been designed for linear problems. However, Harb et al. [16] and Schütz et al. [15] have demonstrated how it can be applied to mixed-integer linear programming (MILP) problems. Neither of both studies has considered a multi-objective optimization (MOO) framework in its optimization of energy hubs at the district scale. ...
... Applying decomposition strategies Wakui et al. [18] have reported a runtime between 2-55.5 hours for a single optimization of 5-100 buildings. Schütz et al. have required [15] 2.6 hours for an optimization of 10 buildings. Reviewed studies have often oversimplified the model considering only the grid operation or the electricity layer [3]. ...
In light of the energy transition, it becomes a widespread solution to decentralize and to decarbonize energy systems. However, limited transformer capacities are a hurdle for large-scale integration of solar energy in the electricity grid. The aim of this paper is to define a novel concept of renewable energy hubs and to optimize its design strategy at the district scale in an appropriate computational time. To overcome runtime issues, the Dantzig–Wolfe decomposition method is applied to a mixed-integer linear programming framework of the renewable energy hub. Distributed energy units as well as centralized district units are considered. In addition, a method to perform multi-objective optimization as well as respecting district grid constraints in the decomposition algorithm is presented. The decomposed formulation leads to a convergence below 20 min for 31 buildings and a mip gap lower than 0.2%. The centralized design enhances the photovoltaic penetration in the energy mix and reduces the global warming potential and necessary curtailment in order to respect transformer capacity constraints.
... Although a centralized control structure is efficient for coordination, it needs a specified communication channel that is costly for practical implementation. In a decentralized microgrid control struc-ture, each component is independently controlled by its local control unit and does not coordinate with other local control units [10,11]. Typically, due to the wide spread topology of power system networks and electrical coupling between power system components, microgrid implementations are a combination of centralized and decentralized control structures. ...
This paper presents an efficient power management, voltage balancing and grid synchronization control strategy to increase the stability and reliability of distributed energy resources (DERs)-based microgrid. The microgrid is composed of Photovoltaic, Double Fed Induction Generator-based wind and diesel generator with critical and non-critical loads. The system model and the control strategy have been developed in Real Time Digital Simulator. The coordination and power management of the DERs in both grid connected and island operation modes is implemented. One distinct challenge of microgrid operation in island mode is the stable control of frequency. A controller is proposed and implemented in the island mode for the diesel generator equipped with the required inertia to maintain the microgrid rated frequency by operating in the isochronous mode. To restore the microgrid back to the utility, the voltage, frequency and phase angle of the islanded microgrid should match with that of the grid network within specified limits to avoid transient instability. Switched capacitor banks are connected at the point of common coupling to balance the voltage for microgrid synchronization. The CIGRE medium voltage test bench system is used to implement the DERs and their controller. The proposed control approach has potential applications for the complete operation of microgrids by properly controlling the power, voltage and frequency in both grid and island modes. The real time digital simulator results verify the effectiveness and superiority of the proposed control scheme in grid connected, island and grid resynchronization scenarios.