A coordinated planning model for power system source-network-load-storage considering multiple types of energy storage

Conventional power systems often neglect the amalgamation of planning and scheduling during their preparatory phases, and their methodologies of construction and operation may fall short of the requisites posed by a future characterized by a high proportion of renewable energy integration. In this paper, an integrated planning and scheduling methodology has been developed for the source-network-load-storage power system, factoring in a diverse array of energy storage modalities. Initially, cost models and operational models are formulated encompassing unit retrofitting, line expansion, demand response (DR), energy storage configuration and operation, and penalties related to solar curtailment. Subsequently, the optimization functions and operational constraints of each facet are harmonized, culminating in a comprehensive coordinated planning and scheduling framework for the ‘source-network-load-storage’ power system. The final phase involves simulation analysis conducted on an IEEE-30 nodes power system case. Results illustrate that the integrated ‘source-network-load-storage’ approach exhibits varied efficacies in exploiting system flexibility resources, contingent upon the distinctive capacity-power configurations of multiple types of energy storage. Prudent calibration of energy storage parameters demonstrates the potential to mitigate network underutilization and enhance overall system economics. This corroborates the viability of the proposed ‘integrated planning and scheduling’ model for the power systems.


Introduction
In recent years, spurred by the evolution of the energy Internet and the strides in intelligent technology, the power system has been undergoing a transformation from the conventional single large-scale centralized power supply paradigm towards a more diversified distributed generation (DG) landscape [1] .However, wind power's susceptibility to meteorological variables renders it volatile [2] , and its intermittent and fluctuating power generation nature can introduce complexities in grid peak management and voltage regulation.This dynamic has elevated the intricacy and fragility of the power system [3]- [5] .The inherent uncertainty linked to renewable energy sources also ushers in challenges like the underutilization of wind and solar resources, voltage instability, frequency instability, and transmission line overloads [6]- [8] .Consequently, realizing a substantial integration of renewable energy sources while maintaining the dependable operation of the power grid underscores the pivotal significance of investigating the coordinated planning of source, network, load, and energy storage within the power system-accounting for diverse energy storage alternatives.Numerous existing studies delve into the flexibility resources inherent in each facet of the source, network, load, and storage components [9]- [15] .However, there is a noticeable scarcity of research concerning the power system that takes into account the amalgamation of unit configurations, grid enhancements, demandside responses, and energy storage.Consequently, the challenge of orchestrating the planning and scheduling of these manifold flexibility resources to synergistically enhance their utilization is a pressing matter in need of resolution.
In response to these challenges, this paper delves into a source-network-load-storage planning framework that encompasses a spectrum of energy storage modalities.Initially, a comprehensive cost model is formulated, unit retrofitting, line expansions, energy storage configuration, and penalties related to solar curtailment.An operational model is subsequently developed, unit retrofitting, power flow model, demand response strategies, pumped storage and energy storage operations [16] .This operational model is then harmonized with the collective objective function and operational constraints of each constituent, culminating in the construction of a holistic source-network-loadstorage coordinated operational and planning model tailored to the power system's dynamics.The ultimate phase of the study involves the simulation and analysis of a source-network-load-storage coordination scheme, considering distinct proportions of diverse energy storage forms, using the IEEE-30 nodal power system case.The results corroborate the efficacy of the proposed 'integrated planning-dispatching' model, validating its pertinence in the context of the power system's intricate dynamics.

Mathematic models
A 'Source-network-load-storage' power system framework facilitates the comprehensive optimization of resources across source, network, load, and storage dimensions during the planning phase.It effectively integrates a spectrum of flexible resources including coal-fired units, grid reconfigurations, centralized energy storage, and demand response (DR), thereby mitigating the uncertainties arising from the substantial incorporation of new energy sources.This section entails the modeling of the constituents within the 'Source-network-load-storage' framework.

Source side
When addressing the 'source' aspect, beyond unit retrofitting, it is essential to consider flexible improvements for existing units.The core objective of unit configuration is to maximize the economic efficiency of the system's power generation.This involves accounting for coal consumption costs linked to meeting the supply load and the operational expenditures tied to initiating and halting unit operations [17] .

Network side
Currently, one of the main reasons for wind and solar energy curtailment is the inadequate capacity of certain transmission lines, leading to congestion in channels for transmitting new energy.Expanding or upgrading transmission lines is an effective approach to improve the integration capacity of new energy sources.This paper considers the expansion cost of the existing transmission lines in the power system [18] .

Load side
In systems characterized by a significant proportion of new energy sources, the integration of demand response (DR) strategies can yield load smoothing benefits.This improvement in the grid's capacity to absorb new energy is achieved by bolstering demand during high-generation periods and curtailing it during low-generation periods for new energy loads [19] .Presently, a prevalent DR strategy involves employing peak-valley differential tariff mechanisms, leveraging time-based tariffs [20] .This strategy diverts a portion of power consumption from peak hours to off-peak hours, effectively achieving peakload shaving, valley-load filling, and load smoothing.
When computing costs, this paper does not factor in cost increments arising from load-side responses.

Storage side
The complex demands of modern grids necessitate a diverse array of energy storage solutions, as no single energy storage system suffices.Integrating multiple energy storage technologies amalgamates the strengths of various approaches, thereby mitigating the weaknesses inherent in individual technologies., amplifying operational efficiency, and reducing costs.
The energy storage model developed in this study incorporates both variable-speed pumped storage units and centralized energy storage.Pumped storage systems offer operational flexibility and reliability, equipped with swift start-stop capabilities.Furthermore, the energy storage capacity of pumped storage systems often surpasses that of other alternatives, boasting low generation costs and economic viability as a clean energy source.However, the construction of pumped storage power plants demands stringent geographical prerequisites and carries a substantial price tag, creating hurdles for expansion.Centralized energy storage pertains primarily to battery energy storage.Battery energy storage provides the flexibility to tailor scale configurations according to project needs, unrestricted by geographic resource limitations, leading to widespread adoption.Nevertheless, battery energy storage entails a relatively shorter operational lifespan and higher upkeep costs.Additionally, it raises safety and environmental concerns, as improper operation could result in pollution and safety incidents.
The currently available different storage systems have their own technical properties.The hybrid energy storage system can combine the advantages of different types of energy storage.Compared to the single-type energy storage system, the hybrid energy storage system can reduce energy wastage, enhance the reliability of the power system, amplify operational efficiency, reduce total system losses and improve the economic efficiency of the system [21]- [25] .
The costs attributed to pumped storage systems encompass both initial investment and operational maintenance expenditures.

PSS PSS,Inv PSS,OP ,
) is the investment operation maintenance cost of the pumped storage systems.( ) ) is the investment and operation maintenance cost of the centralized energy storage plants.

C
is annualized investment cost of centralized energy storage plants.CES  is repair cost of centralized energy storage plants.The total investment and operation maintenance costs of the energy storage systems are as follows: is The investment and operation maintenance cost of the energy storage systems.3. Power system model considering coordinated planning of source-network-load-storage

Objective Function
Considering the power system planning cost of source-network-load-storage coordination, the objective function mainly includes the cost of unit coal consumption and unit modification cost, line expansion cost, energy storage investment and operation maintenance cost.
C is total cost of power system planning considering source-grid-load-storage.G C is retrofit cost when upgrading thermal power units.
L C is cost of line expansion and reconstruction.

Constraints
The source side needs to fulfil the following operational constraints: units' real power upper and lower limit constraints, unit climb constraints, start and stop constraints and hot reserve constraints [17] .The network side needs to fulfil the following constraints in operation: renewable energy grid connection constraints [13] , the power balance equations and power flow equations of the power system.The initial load response to tariff adjustments is subject to a response magnitude limit.Additionally, the aggregate load over a cycle must remain equal to the pre-adjustment level [26] .As for storage side, when formulating the pumped storage model, these constraints encompass upper and lower limits on output power, restrictions associated with operational state transitions, limitations on the maximum daily start-stop cycles, and requirements for maintaining initial and final daily power balance.Centralized energy storage models usually incorporate investment capacity limitations and operational constraints, simplifying the intricate internal configuration details.This simplification streamlines the process of calculation and analysis, thereby effectively illustrating the role of centralized energy storage within the power system.The storage side needs to fulfil the energy storage's real power upper and lower limit constraints, the operating process state constraints, the maximum number of daily starts and stops constraints and investment capacity constraints [27] .

Decision variables
When conducting calculations, the chosen construction site for the pumped storage power plant remains constant due to the stringent geographical prerequisites associated with such facilities.It is assumed that the geographical conditions at the designated construction node for the pumped storage power plant fulfill the necessary criteria.The capacity and power output of the pumped storage facility are designated as decision variables.The prospective sites for centralized energy storage installation are determined based on the locations of new energy generation stations, with decision variables encompassing the determination of whether to establish centralized energy storage at these locations and configuring the capacity-power parameters of centralized energy storage.Furthermore, the variables encompass aspects such as the output and operational status of thermal power units and energy storage systems, the load and electricity price following demand response (DR) implementation, along with the decision variables pertaining to unit retrofitting and transmission line expansion.

Algorithmic model
Due to the inclusion of both continuous variables, such as energy storage capacity and power, as well as integer variables, such as unit retrofitting and transmission line expansion, in this paper's formulation, and given that both the objective function and constraints are linear in nature, a mixedinteger linear programming model is constructed.This model is solved using MATLAB by utilizing the YALMIP+Gurobi solver.

Case study
The IEEE-30 node power system model employed as an illustrative example in this study is shown in figure 1.The grid comprises a load of 277MW and a collective generator capacity of 477.5MW.Additionally, the power system is interconnected with four centralized PV stations, each boasting a capacity of 150MW.With a cumulative new energy generation penetration rate of 48.748%, a pumped storage power station is strategically situated at node 4. Distributed energy resources with capacities of 20MW, 30MW, 30MW, and 20MW are respectively connected to nodes 11, 17, 23, and 25, contributing to a combined capacity of 100MW.For the new energy field station, an energy storage system is planned for installation at a specific node.Throughout the entire network, there are a total of 41 candidate expansion lines under consideration, along with 6 candidate generators earmarked for retrofitting.
The computed results of the power system's source-network-load-storage coordinated planning model considering multiple types of energy storage are illustrated in figure 2.
The model is solved using the source-network-load-storage coordinated optimization method proposed in this paper.The results show that the new energy abandonment rate is 2.955%, and the total amount of abandoned power is 3.315×10 4 MWh.The results of the line expansion and unit planning are obtained as shown in figure 2. The generators at nodes 1, 2, and 13 are chosen to carry out the unit reconstruction and upgrading.A total of 8 lines are expanded and upgraded.The results show that all the new energy field stations are configured with energy storage devices with a total configuration of 173.60 MW and 690.92 MWh and a configuration of 9.17 MW and 917 MWh of pumped storage.
The planning cost comparisons for each scheme are shown in table 1 and table 2, with capacity in MWh, power in MW, and cost in billions of yuan:  Table 1 contrasts the planning results between scenarios that employ only centralized energy storage and those that involve both pumped storage and centralized energy storage.Scheme 1 adopts a source-network-load-storage coordination and operational planning method where the parameters related to pumped storage and centralized energy storage serve as planning objectives.The power capacity of energy storage systems and the construction nodes for centralized energy storage are outcomes of this plan.Scheme 2 focuses solely on centralized energy storage and maintains the total power and capacity of centralized energy storage unchanged as planning results.5 distinct schemes have been formulated: Scheme 1 embraces a source-network-load-storage coordination and operational planning approaches, wherein the parameters linked to pumped storage and centralized energy storage are adopted as pivotal planning objectives.The power capacity of energy storage systems and the timing of construction for centralized energy storage facilities result from this strategy.Conversely, Schemes 2 through 5 employ source-load-storage coordination and operational planning methodologies, omitting the incorporation of parameters associated with pumped storage and battery energy storage as explicit planning objectives.In these instances, the power capacity of energy storage systems and the scheduling of construction for centralized energy storage facilities are held as fixed constants.

Conclusion
This paper centers on the coordinated operational planning model for the power system, considering various types of energy storage.The methodology is tackled by leveraging YALMIP+Gurobi solver on the IEEE-30 node power system case.Drawing insights from the experimental results, the subsequent conclusions can be drawn.
Firstly, based on the results from table 1, it is evident that using multiple types of energy storage is more economically advantageous than relying on a single type of energy storage.This improvement is attributed to the synergistic coordination of pumped storage and centralized energy storage in the multi-type scenario, enhancing system flexibility, reducing curtailed renewable energy, consequently lowering curtailment penalties, and improving overall economic viability.
Secondly, the analysis of planning results in table 2 reveals that, in comparison to Scheme 1, Scheme 2 also involves the integration of energy storage across all new energy generation sites.However, there are variations in the energy storage capacity and power compared to Scheme 1, leading to an increase in overall planning costs.This highlights the influence of differing allocations of energy storage power and capacity on planning outcomes, even when maintaining the same number of construction nodes and the total power and capacity of energy storage devices.For Schemes 3 and 4, the overall planning costs increase while retaining the total capacity and power of configured energy storage due to the construction of centralized energy storage units at only three nodes.This emphasizes how altering the number of construction nodes impacts planning outcomes, despite maintaining the same total power and capacity of energy storage devices.In Schemes 4 and 5, under constant total capacity and power of configured energy storage, a relationship between the number of nodes for constructing centralized energy storage and the overall planning cost emerges, provided that the pumped storage capacity and power remain unchanged.These results underscore the importance of considering different configurations of energy storage types during the planning process to enhance system economics and reduce curtailment of renewable energy.Therefore, practical power system planning should encompass a comprehensive evaluation of factors such as new energy grid integration capacity, transmission pathway upgrades, unit retrofitting and enhancements, energy storage allocation, and demand response control.Additionally, incorporating diverse types of energy storage planning configurations is essential for optimizing system operational efficiency.
In the context of the coordinated operational planning model for the source-network-load-storage power system, incorporating multiple types of energy storage, rational configuration of energy storage parameters can ameliorate light abandonment phenomena within the power system and enhance the overall economic efficiency of the system.Thus, these findings affirm the effectiveness of the proposed 'integrated planning-dispatching' model for power system planning.

Figure 1 . 2 .
Figure 1.IEEE-30 node power system Figure 2. Power system planning results the pumped energy storage systems of the k pumped energy storage in scenario s.
PSS,Inv C is annualized investment cost of pumped energy storage systems.s  is the set of typical daily operational scenarios s. s D is typical daily operating scenario s in days of the year.PSS P is discharge power of pumped energy storage unit k in scenario s.

Table 1 .
The planning results for single-type energy storage and multi-type energy storage.

Table 2 .
Different ratios of multitype energy storage planning results.