Reimagining Grid Flexibility in a Constrained Power System: A Techno-Economic Evaluation of Battery Storage, Coal Performance, and Transmission Bottlenecks in South Africa ^†

Abstract

South Africa’s power system has been characterised in recent years by declining coal fleet performance, accelerated renewable energy deployment because of system unreliability, and persistent delays in transmission expansion to replace ageing infrastructure (and enable new generation). These structural pressures have created a growing need for grid flexibility, particularly as renewable energy becomes the dominant source of new generation. This paper presents a techno-economic assessment of battery energy systems (BESSs) within a constrained national context, using three scenarios: a policy-aligned baseline (0), high-demand/moderate renewable growth (1), and a constrained transition pathway (2). They were modelled using a validated least-cost capacity expansion and dispatch framework incorporating updated assumptions on coal availability, transmission delivery constraints, renewable build caps, and demand trajectories. The results show that each scenario produces a distinct system stress mechanism. In Scenario 1, rapid renewable expansion leads to surplus-driven curtailment and increased flexibility requirements, with BESS delivering substantial operational value. In Scenario 2, coal fleet underperformance, procurement limits, and transmission congestion create energy-deficit conditions despite low demand, resulting in the highest unserved energy and congestion-driven curtailment. However, Scenario 0 is comparatively less stressed, but displays minor energy adequacy shortfalls after 2030, indicating that the baseline is not fully adequate under strict planning criteria. Ultimately, across all scenarios, storage and transmission expansions are shown to be complementary investments, which are jointly required to mitigate system-wide inefficiencies.

1. Introduction

1.1. Background: South Africa’s Energy Sector and the Role of BESS

The country’s electricity sector is undergoing a rapid and complex transition. The historical dominance of coal is increasingly challenged by declining energy availability, ageing infrastructure, and faster decommissioning schedules driven by the country’s emissions-reduction target [1]. At the same time, renewable energy, primarily solar PV and wind, has become the lowest-cost source of new generation and is expanding steadily through utility-scale procurements such as the Renewable Energy Independent Power Producer Procurement Programme (REIPPPP) and behind-the-meter installations [2].

Battery energy systems (BESSs) have emerged as a central flexibility resource capable of addressing many of these challenges [3]. BESSs can mitigate ramping stress, reduce renewable curtailment, provide peak capacity, supply contingency reserves, and enhance local grid stability [4]. These functions are particularly valuable in this country, where transmission constraints in renewable-rich provinces, persistent coal unpredictability, and increasing system volatility amplify the need for flexibility [5]. National planning documents, including the Integrated Resource Plan (IRP) and the Transmission Development Plan (TDP), increasingly acknowledge the critical role of BESSs in providing flexibility and supporting the integration of renewable energy [6,7]. However, the optimal sizing, siting, timing and full operational value of BESSs under different future system conditions remain insufficiently analysed and quantified [4,5].

1.2. Motivation for an Integrated Modelling Framework

This study is motivated by the need for a coherent, multi-layered modelling framework that can capture both the long-term investment dynamics and short-term operational realities of the evolving South African power system. Traditional capacity expansion models are well-suited for planning generation portfolios, but they often lack the temporal resolution required to assess renewable variability, coal fleet instability, intraday ramp behaviour, and the operational value of storage [8]. Conversely, chronological dispatch models can capture operational complexity but require system configurations as inputs [8].

To address this gap, this paper uses an integrated OSeMOSYS and IRENA FlexiTool workflow developed as part of an ongoing research project at the University of Cape Town. The research framework evaluates renewable integration pathways across Southern Africa under multiple uncertainties. Thus, this paper uses the South Africa-only subset of that framework to quantify how coal fleet performance, renewable expansion, and transmission bottlenecks interact to shape flexibility needs and the overall system value of BESS.

1.3. Research Gap and Objectives

Although several studies have explored storage deployment and renewable integration in South Africa, three critical gaps remain: (1) A lack of joint analysis of coal performance, transmission constraints, and renewable build limits within a single modelling framework. (2) Insufficient operational resolution in many planning studies limits the ability to quantify ramping challenges, curtailment dynamics, and peak adequacy. (3) Limited evaluation of the system value of BESSs under differing structural futures, particularly under conditions of grid constraint and coal fleet underperformance.

To address these gaps, this study applies a scenario-based techno-economic modelling approach to achieve the following objectives:

• Assess the operational and economic value of BESSs in mitigating renewable curtailment, supporting peak supply, and reducing reliance on diesel/Open-Cycle Gas Turbine (OCGT) generation.
• Quantify how flexibility needs evolve under different combinations of coal performance (optimal and worst case), transmission availability, and renewable build trajectories.
• Identify the dominant system stress drivers in each scenario: surplus-driven, ramp-driven, or infrastructure-driven and evaluate the policy implications for planning.

2. Literature Review

South Africa’s electricity sector has endured prolonged structural strain, marked by stagnant generation capacity, ageing coal infrastructure, and chronic operational instability. The national grid historically relied on a concentrated fleet of large baseload coal-fired power stations, many of which were commissioned between the 1970s and 1990s [2]. As these plants have aged, their energy availability factors have steadily declined, contributing to the persistent load shedding that has characterised the sector since 2010 and intensified significantly after 2018 [9]. Load shedding has become a defining feature of the power system, with far-reaching economic impacts and increasing public and industrial pressure for structural reform [10].

In response to this deteriorating supply–demand balance, the government introduced the REIPPPP in 2011 [11]. REIPPPP marked a significant shift from a coal-dominated system toward competitive, privately financed renewable energy procurement. Successive bid windows catalysed significant deployment of solar photovoltaic (PV) and wind energy, thereby establishing South Africa as a leading renewable energy market in Sub-Saharan Africa [11]. However, delays in bid windows, escalating grid constraints, and uncertainty in policy direction, particularly around the IRP, have slowed the pace of new generation coming online, with the current build rate for all renewable projects sitting at less than 2 GW per year [4,7].

Recognising the urgent need for flexibility and reliability, the government launched a BESS procurement programme in 2024 [12]. Under this program, up to 11 GWh of storage capacity is being tendered as part of an independent power producer (IPP) framework, signalling the first large-scale, government-backed push for storage deployment in the country.

Parallel to these developments, the transmission network has also emerged as a binding constraint on renewable integration [13]. Provincial grid saturation, especially in renewable-rich areas such as the Northern Cape, Western Cape, and Eastern Cape, has prevented additional renewable projects from connecting despite abundant resources and investor interest [13]. The TDP identifies urgent reinforcement needs that exceed current funding and delivery capabilities. Additionally, evolving discussions around grid access, capacity allocation rules, and transmission planning reflect a context in which grid constraints, rather than energy scarcity, are increasingly the main barrier to the transition [11].

Despite these developments, the existing literature tends to examine coal performance, renewable expansion, transmission constraints, and storage deployment separately [9]. Very few studies assess how these factors interact under multiple plausible future pathways. There remains limited integrated analysis that combines long-term capacity expansion with hourly chronological dispatch to quantify the operational implications of coal instability, capped renewable build, and transmission congestion across scenarios [14].

While this study provides a detailed operational and economic assessment of the country’s future power system under multiple uncertainty pathways, several limitations must be acknowledged. The modelling framework represents transmission constraints through provincial capacity envelopes rather than full nodal or alternative current (AC) load-flow analysis, approximating local grid congestion and stability rather than explicitly simulating them. Accordingly, the study likely underestimates the congestion-driven curtailment and unserved energy that would arise under substation-level deliverability constraints. FlexiTool captures hourly chronological dispatch but does not model unit commitment, inertia constraints, frequency response, or even voltage stability in granular detail. The analysis also assumes perfect foresight in investment and dispatch, which may not reflect real-world procurement delays and market behaviour. Furthermore, the scenarios focus on three dominant structural uncertainties: coal performance, renewable build limits, and transmission bottlenecks, while other variables such as fuel price volatility, gas infrastructure development, and long-duration storage options are not exhaustively explored.

3. Methodology

This study applies a two-stage techno-economic modelling framework that integrates long-term system expansion (via OSeMOSYS [The Open-Source Energy Modelling System, available online: https://osemosys.github.io/, accessed on 13 October 2025]) with detailed chronological operational simulation (via FlexiTool [IRENA FlexiTool v3.32.0, available online: https://github.com/irena-flextool/flextool, accessed on 13 October 2025]). This structure enables analysis of both structural investment decisions and hourly dispatch behaviour under different coal, renewable, and transmission trajectories.

3.1. Overview of the Integrated OSeMOSYS-FlexiTool Framework

The modelling workflow consists of three significant steps:

i.

Define the input assumptions: Demand forecasts, coal energy availability factors (EAFs), renewable build constraints, BESS caps, transmission capacity envelopes, fuel costs, and policy parameters.

ii.

Run the OSeMOSYS capacity expansion model: Determine the optimal generation, storage, and retirement trajectories over 2025–2035.

iii.

Run the FlexiTool hourly dispatch simulation: Evaluate the operational performance, flexibility needs, curtailment, unserved energy, and diesel reliance.

3.2. OSeMOSYS Capacity Expansion Model

OSeMOSYS is a long-term linear optimisation model that identifies the least-cost mix of generation and storage technologies to meet future electricity demand [15]. It operates on annual investment periods with representative time slices for operational system balancing.

3.3. FlexiTool Chronological Dispatch Model

FlexiTool is a simulation engine that performs hourly dispatch for an entire year, capturing variability, ramping, peak stress, curtailment and flexibility requirements [16].

The model structure designed for this study included the following parameters:

• Temporal resolution: 8760 h/year;
• Inputs: Generation fleet from OSeMOSYS;
• Constraints: Ramping limits, minimum stable levels, storage state of charge (SOC) dynamics, round-trip efficiencies, provincial transfer limits, and load curves and renewable profiles;
• Key outputs: Hourly dispatch by technology, renewable curtailment, storage cycling and SOC trajectories, peak adequacy and reserve shortfalls, unserved energy, diesel/OCGT utilisation, ramp rate profiles, and system operating costs.

4. Scenario Design

Three scenarios were developed to evaluate the country’s power system performance under divergent coal availability trajectories, renewable energy build rates, transmission constraints, and demand outlooks over the 2025–2035 horizon. The scenarios reflect the system uncertainties that exert the most significant influence on flexibility needs: coal performance and retirement, renewable and BESS deployment constraints, transmission expansion and provincial grid capacity, and electricity demand trajectory.

Scenario 0—Policy-Aligned Baseline

Scenario 0 represents a reference trajectory broadly consistent with the current national planning instruments, including the IRP and TDP. The core assumptions are:

• Demand: Moderate growth based on the 2025 IRP [7].
• Coal EAF: Stabilises around current levels and assumes overperformance throughout the study period.
• Renewable Build: Solar PV and wind additions aligned with REIPPPP delivery expectations.
• Storage Deployment: Moderate BESS growth, including both utility-scale and grid-located storage.
• Transmission Expansion: Delivered according to TDP schedules, with no significant delays.

Scenario 1—High-Demand, High-Build Expansion Case

Scenario 1 explores an optimistic future driven by accelerated economic recovery, electrification trends, and rapid renewable energy expansion. Core assumptions:

• Demand: Significantly higher demand and peak growth.
• Renewable Build: High and largely unconstrained solar and wind additions, supported by strong embedded generation.
• Storage Deployment: Fast BESS deployment to support flexibility.
• Coal EAF: Fluctuates between 68% and 70%.
• Transmission Expansion: Reinforcements keep pace with renewable energy deployment; provincial caps are non-binding.

Scenario 2—Constrained Transition Pathway

Scenario 2 represents a pessimistic, stress-case future characterised by structural constraints across multiple areas. This scenario evaluates the adequacy and operational resilience of the system under declining coal performance, constrained renewable growth, and transmission congestion. The core assumptions are:

• Demand: Low demand growth due to prolonged economic stagnation.
• Renewable Build: Annual additions are capped at roughly 2 GW due to procurement delays and financial constraints.
• Storage Deployment: Limited early deployment, resulting in long-term flexibility deficits.
• Coal EAF: Continued deterioration without recovery.
• Transmission Expansion: Severe provincial transmission bottlenecks, particularly in the Northern Cape, Western Cape, and Eastern Cape.

5. Results and Discussions

For coal performance across all scenarios, two cases were modelled: a base case applied in Scenarios 0 and 1; and an assumed moderate overperformance, with coal EAF fluctuating between 68% (the average reported by Eskom for 2025) and 70%. The second case, used for Scenario 2 as a worst-case representation, assumed a progressive decline in coal EAF from 68% to 55% by 2035, indicating severe underperformance.

Across all scenarios, South Africa’s generation mix undergoes a substantial structural shift between 2025 and 2035, with coal declining, distributed renewables expanding, and battery storage emerging as a critical flexibility resource. Coal capacity falls uniformly from 40–41 GW in 2025 to 27 GW by 2035, reflecting planned decommissioning and declining performance. In contrast, solar PV (embedded + utility-scale) increases across all scenarios, albeit at different rates: Scenario 0 reaches approximately 46.5 GW by 2035, whereas Scenarios 1 and 2 reach slightly lower capacities (38–41 GW) due to differences in the sequencing of renewable energy deployment and flexibility constraints. A similar trend is observed for wind generation capacity, which expands to 20.2 GW by 2035 in Scenario 0, compared with 19.2 GW in Scenario 1, and 18.6 GW in Scenario 2. The most pronounced divergence occurs in battery storage deployment. Starting from just 0.4 GW in 2025, battery energy storage capacity grows to 10.2 GW by 2035 in Scenario 0. However, its expansion is considerably slower in Scenarios 1 and 2, where installed capacity reaches 7.0 GW and 8.8 GW, respectively. This variation in deployment is driven by differences in system flexibility requirements, levels of renewable energy curtailment, and transmission grid congestion across the scenarios. Gas-fired generation capacity remains limited in all cases until the latter part of the decade. Combined-Cycle Gas Turbine (CCGT) capacity begins to emerge from 2030 onward in Scenarios 0 and 1, while its deployment occurs slightly later in Scenario 2. Overall, the installed capacity trends demonstrate that while renewable energy growth is broadly consistent across scenarios, the pace of energy storage expansion and the system’s ability to absorb renewables are strongly shaped by underlying transmission constraints, coal decline trajectories, and policy execution, resulting in distinctly different energy system compositions by 2035.

Table 1. Energy balance—Scenario 0.

Year	Load (TWh)	Generation (TWh)	Net Balance (TWh)	Imports (TWh)	DSR (TWh)	Unserved Energy (%)	Dumped Energy (%)
2025	239	238	1	0	1	0.000	0.000
2026	245	243	2	1	1	0.000	0.000
2027	253	248	5	2	3	0.000	0.000
2028	261	253	8	2	6	0.000	0.000
2029	268	258	10	5	5	0.000	0.000
2030	273	263	10	5	5	0.028	0.000
2031	279	268	11	7	5	0.032	0.000
2032	287	273	14	9	5	0.050	0.000
2033	295	279	16	11	5	0.023	0.000
2034	303	284	19	13	5	0.000	0.000
2035	313	290	23	17	6	0.000	0.000

,

Table 2. Energy balance—Scenario 1.

Year	Load (TWh)	Generation (TWh)	Imports (TWh)	DSR (TWh)	Unserved Energy (%)	Dumped Energy (%)
2025	239	238	1	1	0.000	0.000
2026	245	243	1	1	0.000	0.000
2027	253	248	3	2	0.000	0.000
2028	261	253	6	2	0.000	0.000
2029	267	258	6	3	0.000	0.000
2030	271	263	4	4	0.010	0.000
2031	277	268	4	4	0.054	0.000
2032	284	273	5	6	0.102	0.000
2033	291	279	5	7	0.090	0.000
2034	300	284	5	11	0.101	0.000
2035	308	290	5	13	0.029	0.000

and

Table 3. Energy balance—Scenario 2.

Year	Load (TWh)	Generation (TWh)	Imports (TWh)	DSR (TWh)	Unserved Energy (%)	Dumped Energy (%)
2025	239	238	1	1	0.000	0.000
2026	244	242	2	1	0.000	0.000
2027	250	245	3	2	0.000	0.000
2028	257	249	6	2	0.000	0.000
2029	261	253	6	3	0.044	0.000
2030	270	256	4	9	0.133	0.000
2031	276	260	4	11	0.135	0.000
2032	284	264	5	15	0.225	0.000
2033	288	268	5	15	0.111	0.000
2034	292	272	5	15	0.025	0.000
2035	298	276	6	16	0.014	0.000

show the energy balance results across all scenarios, including demand-side response (DSR) and underserved energy.

Across all scenarios, total system demand increases steadily from 239 TWh in 2025 to between 298TWh and 313 TWh by 2035. The energy balance results for Scenarios 0, 1, and 2 are presented in Table 1, Table 2 and Table 3, respectively. However, the ability of the generation fleet to meet this growing demand diverges significantly depending on the underlying system constraints. In Scenario 0, electricity generation closely matches demand over the entire planning horizon. No unserved energy is observed until the period 2030–2032, during which only minor shortfalls (less than 0.05% of demand) occur before the system stabilises as renewable energy and storage capacity expand. In contrast, Scenario 2 exhibits progressively worsening adequacy performance. Although load growth remains moderate, constrained deployment of renewable energy and declining coal-fired generation increase reliance on imports and demand-side response. Consequently, unserved energy rises from 0.04% in 2029 to peak values of 0.23–0.31% during 2032–2033. Scenario 1 experiences the greatest challenges in flexibility. It is characterised by consistently higher battery charging requirements, increased pumped storage utilisation, and the highest levels of unserved energy, exceeding 0.30% in the early 2030s. This reflects a situation in which rapid load growth outpaces the expansion of firm and flexible generation sources. Notably, dumped energy remains zero across all scenarios, indicating that system imbalances are not driven by excess generation. Rather, they arise from structural and temporal mismatches between available generation capacity, transmission network deliverability, and peak demand requirements. Overall, the energy-balance results demonstrate that reliability risks are influenced not only by increasing demand but also by constraints on renewable energy integration, transmission capacity limitations, and insufficient mid-merit flexibility. These challenges are most pronounced in Scenarios 1 and 2.

6. Conclusions

This paper applied an integrated modelling framework to assess the evolution of South Africa’s power system under three contrasting futures characterised by varying levels of coal plant performance, renewable energy deployment, energy storage expansion, and transmission network availability. Scenario 0 demonstrates that, when planning assumptions are realised, the system remains broadly stable, characterised by moderate electricity prices, low levels of unserved energy, and increasing renewable energy penetration supported by substantial growth in battery energy storage. In contrast, Scenario 2 (pessimistic case) illustrates how constrained transmission expansion and limited deployment of renewable energy can rapidly undermine system adequacy. This results in increased reliance on diesel generation and electricity imports, leading to higher regional electricity prices and persistent levels of unserved energy despite relatively modest demand growth. Scenario 1 also experiences significant operational challenges, highlighting how accelerated demand growth can exacerbate flexibility shortages when the deployment of renewable generation and energy storage fails to keep pace.

Across all scenarios, battery energy storage consistently emerges as a critical enabler of renewable energy, peak demand support, and overall system stability. At the same time, transmission constraints have been shown to pose a major long-term risk, limiting the benefits of renewable energy sources and increasing overall system costs. The results, therefore, underscore a clear strategic priority for South Africa’s energy transition: coordinated investment in transmission infrastructure, accelerated procurement of renewable energy and storage technologies, and a well-managed coal phase-down strategy. A key policy implication of this analysis is the need for a dedicated, multi-year BESS procurement and deployment framework that aligns storage investments with renewable energy expansion and coal retirements. Such a framework would ensure that storage capacity is deployed where it delivers the highest system value, thereby enhancing reliability, reducing system costs, and supporting a secure and sustainable energy transition.