Electrical hubs: An effective way to integrate non-dispatchable renewable energy sources with minimum impact to the grid
Graphical abstract
Introduction
Integrating renewable energy technologies into the electricity grid is gradually getting popular due to rapid depletion of fossil fuel resources and global concerns on greenhouse gases emissions and nuclear energy. Several countries have their own goals with different time lines in this regard. For example, Germany has a goal to cover 50% of the generation system using renewable energy by 2030 [1], while in Finland it is 38% by 2020 [2]. Switzerland is expected to phase-out nuclear energy by 2035 by increasing the energy efficiency and the share of renewable energy sources. In Sri Lanka, it is expected to increase the share of non-conventional renewables, such as SPV and wind energy, up to 20% by the end of 2020. Recent studies highlight that distributed generation using solar PV (SPV) and wind energy is promising to foster the renewable energy penetration in the market [3], [4].
Energy systems fully driven using renewable energy sources is a dream that wider community of researchers try to make a reality [5], [6], [7], [8], [9]. Replacing dispatchable energy sources driven by fossil fuel through distributed SPV, wind and biomass/bio energy sources is the major challenge in this context. Mismatch in time of peak demand and generation due to stochastic nature of wind speed and solar radiation as well as of electricity demand makes the renewable energy integration process difficult [10], [11]. Integration of dispatchable energy sources, energy storage and converting into hybrid renewable energy systems is a cost effective approach in increasing the reliability during the renewable energy integration process. Further, this helps to amalgamate energy sources with higher seasonal variation in energy potential [12], [13] with less impact to the grid. More importantly, this is the starting point of minimizing the contribution of dispatchable energy sources based on fossil fuels, which makes existing energy systems more eco-friendly and sustainable [10], [14]. However, optimum designing of such energy systems is a challenging task.
Several research groups have focused on optimizing grid-integrated hybrid energy systems. Fathima and Palanisamy [15] provides a detailed review of the major recent works on grid-integrated hybrid energy systems. Two different approaches can be used in this context to optimize the system design considering the dispatch simultaneous.
- (1)
Energy system is expected to operate in finite set of states (finite state machines) in which operating conditions for the dispatchable energy sources and storage is defined. Subsequently, state transfer function is optimized along with the energy system (sizing problem) [16], [17], [18], [19].
- (2)
Optimum operating conditions for dispatchable energy sources and storage is obtained for each time step considering these as decision space variables [20], [21], [22], [23], [24]. This can be further classified into two groups, depending whether dispatching is optimized as time depended small scale problems or globally as a unique large size problem as explained in Ref. [25].
Both these methods are coming with their strengths and weaknesses. The first method can consider non-linear models (considering valve point effect etc.) easily for energy conversion processes without simplification. Furthermore, first method can present the performance of the system (for 8760 time steps) with less computational time. However, the number of possible states that the system could operate increases exponentially with the complexity of the energy flow within the system (especially for poly-generation with multiple dispatchable energy sources and storages). Second method is more suitable when considering complex energy systems with multiple dispatchable sources and storage. However, computational time and the resources required become extremely high when using this method. According to Evins [22] optimization time can reach up to nine days when considering second method while Pruitt et al. [24] report that there are limitations in handling a time horizon due to the increase of decision space variables. Further, simple linearization of objective functions can influence the results of the optimization problem significantly [26]. Hence, designing energy systems with simple energy flow such as hybrid energy systems and grid tied hybrid energy systems tends to use the first method while the second method is used for poly-generation [20], [21], [22], [23], [24].
The first part of the manuscript introduces a novel optimization algorithm to design grid integrated electrical hubs extending the first method based on finite states. Electrical hub is a simplified version of multi-energy hubs (amply studied in recent literature considering its operation [27], [28], [29], [30], [31] and design optimization [22], [23]). The electrical hub consists of wind turbines, SPV panels, battery bank and an Internal Combustion Generator (ICG) which is designed to operate as a grid-tied hybrid energy system. Finite state machines have been amply used to optimize energy systems with similar architecture to electrical hubs and hybrid energy systems which are operating both stand-alone and grid integrated modes [18], [19], [32], [33], [34], [35]. In previous studies of the authors, [17], [36], multi objective optimization and multi criterion decision making related to stand-alone hybrid energy systems were taken into discussion without grid interactions. A comprehensive review about optimization techniques used on this regard can be found in Ref. [37]. Grid integrated hybrid energy with a similar architecture to electrical hubs have been also optimized by extending the dispatch strategy used to optimize stand-alone systems [38], [39]. As a result, the state of the charge of the battery bank and the price of electricity in the grid has not been considered in the dispatch strategy although these factors can significantly influence the cash flow of the system according to Ref. [40], [41]. Number of states that system could operates increase notably when considering the energy storage, dispatchable energy sources and grid interactions simultaneously. In order to address these issues, this study introduces a novel bi-level dispatch strategy coupling fuzzy logic and finite state machines in order to optimize system design along with dispatch strategy. Fuzzy logic has been amply used in dispatch optimization of hybrid energy systems [42], [43], [44], [45] which is considered as one of the most promising techniques by the recent review on energy management strategies for hybrid energy systems [46]. However, for the best of author’s knowledge fuzzy logic has not been used for dispatching to support design optimization (system sizing problem) before, which can be used as an attractive method to address the limitations in the existing design optimization process.
The second part of the manuscript presents a detailed assessment on the potential of electrical hubs to integrate SPV and wind energy with a minimum impact to the grid (making the energy system to be autonomous while minimize the energy export and import to and from the grid). Integrating higher fractions of non-dispatchable renewable energy technologies while operating at higher autonomy levels (minimum grid interactions) is a difficult task [47], [48]. According to Ueckerd et-al [49] direct integration of higher fractions of non-dispatchable renewable energy sources above 30% is beyond the reach due to the limitations in the grid. A quantitative and qualitative analysis about the potential of integrated energy systems (such as electrical hubs) to extend the SPV and wind energy integration (with minimum impact to the grid) is missing in literature besides its timely importance. This moves beyond design optimization where detailed assessment of the electrical hub is required. To achieve this objective, Pareto optimization is conducted in this study considering Levelized Energy Cost (LEC) and Grid Interaction (GI) level (extending the definitions in Ref. [47], [48]) as objective functions. Decision space variables related to the system sizing problem and dispatch strategy are considered as decision space variables to be optimized. Sensitivity of the mode of grid interactions (importing and exporting electricity from the grid), the price of electricity and the curtailments in the grid and role of ICG and energy storage on SPV and wind energy integration are taken as the aspects to be assessed.
The manuscript is arranged in the following manner; a novel method to optimize electrical hubs is proposed in the first part of the manuscript extending existing methods to optimize grid integrated hybrid energy systems which is discussed in Sections 3 Mathematical model for the electrical hub, 4 Novel dispatch strategy and simulation, 5 Optimization of the system design and dispatch strategy. The second part (Section 6) is devoted to evaluate the potential of electrical hubs to increase the SPV and wind energy contribution with a minimum impact to the electricity distribution grid considering the recent and future changes in the grid.
Section snippets
Overview of the problem
This section provides an overview about the concept of electrical hub within the framework of distributed generation and the computational tool developed to assess electrical hubs.
Mathematical model for the electrical hub
The mathematical model developed in this work consists of several parts devoted to analyze the energy and cash flow of the system, grid interactions and power supply reliability. This is used to formulate LEC and Grid Integration (GI) level which are considered as objective functions (F ∈ Ƒ: set of objective functions) to be optimized. Decision space represents variables of the system design and operation (dispatch strategy); the system design variables consist of the type (technology) of SPV
Novel dispatch strategy and simulation
Seasonal variations of the renewable energy potential, demand and the dispatch strategy of the system notably influence the system sizing [70]. Hence, simulation of the system, considering hourly variation of renewable energy potential, grid conditions and demand is vital. Meanwhile, power generation using dispatchable energy sources and energy interactions with storage and grid need to be carried out in an optimum way. This mean that dispatch strategy needs to be optimized with the system
Optimization of the system design and dispatch strategy
Designing electric hubs integrated to the grid is challenging due to a number of reasons as discussed before. A heuristic algorithm has been amply used in the literature [19], [32], [33], [34], [39], [78], [79], [80] and shown to be much efficient when optimizing these systems when compared to enumerative methods [81] which are used in existing software such as Homer [82]. A detailed comparison of these methods can be found in recent reviews on hybrid energy system designing [79], [83]. This
Results and discussion
Selecting optimum combination of energy technologies, storage becomes vital in integrating SPV and wind energy into electrical hubs. Autonomy of the system needs to be maximized in integrating renewable energy technologies while minimizing the lifecycle cost of the system. Pareto fronts obtained in Section 5 considering LEC and grid integration level is useful in this regard. These Pareto fronts are used in this section to analyze
- (1)
Sensitivity of imports, exports and both to lifecycle cost,
Conclusions
This focuses on evaluating the potential of electrical hubs in integrating non-dispatchable renewable energy technologies such as SPV panels and wind turbines with minimum impact to grid. A novel optimization algorithm in introduced with the support of a bi-level dispatch strategy to optimize electrical hubs considering both real time price and curtailments for import and export in the grid. A gray model based on fuzzy logic is introduced to control the operation of ICG in the primary algorithm
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