Renewable-only Virtual Power Plants

• Renewable-only Virtual Power Plants (RVPPs) are aggregations of non-dispatchable renewable resources and flexible demand designed to participate competitively in energy, reserve, and intraday markets.
• The literature distinguishes strict wind/PV-only models from broader renewable-based formulations that include dispatchable renewables like hydro and biomass, influencing market scheduling and flexibility.
• RVPP market strategies rely on robust and stochastic optimization to manage forecast uncertainty, optimize profit margins, and provide reliable grid support through dynamic reserve provision.

Searching arXiv for the cited RVPP papers to ground the article in current research metadata. {"query":"all:(\"Renewable-only Virtual Power Plant\" OR \"renewable-based virtual power plant\" OR \"Flexible Robust Optimal Bidding of Renewable Virtual Power Plants in Sequential Markets\" OR \"Single-level Robust Bidding of Renewable-only Virtual Power Plant in Energy and Ancillary Service Markets for Worst-case Profit\" OR \"Assessing Value of Renewable-based VPP Versus Electrical Storage\")","max_results":10} Searching for specific arXiv IDs referenced in the source material. {"query":"id:(Nemati et al., 2024) OR id:(Nemati et al., 2024) OR id:(Nemati et al., 30 Jul 2025) OR id:(Nemati et al., 16 Feb 2026) OR id:(Zhu et al., 10 Apr 2025) OR id:(Chen et al., 2020) OR id:(Oladimeji et al., 2021) OR id:(Oladimeji et al., 2021)","max_results":20} Renewable-only Virtual Power Plants (RVPPs) are virtual power plants whose portfolio is organized around renewable resources and renewable-compatible flexibility, but the literature uses the term with two distinct scopes. In a strict sense, RVPPs are aggregations without fossil generation, often centered on non-dispatchable renewable energy sources and flexible demand; in a broader sense, many models labeled renewable-based or renewable-only also include dispatchable renewable units such as hydro, biomass, or concentrated solar power with thermal storage, and sometimes storage-like demand-side resources, in order to create a market-facing, reserve-capable aggregate entity (Nemati et al., 2024, Nemati et al., 30 Jul 2025). Across these formulations, the common objective is to transform geographically distributed, uncertain renewable injections into a coordinated participant in energy, reserve, and intraday markets, or into a dynamically controlled grid-supporting entity (Oladimeji et al., 2021, Marinescu et al., 2021).

1. Definition, scope, and terminology

A central issue in the RVPP literature is terminological. Several models are explicitly “renewable-based” rather than strictly renewable-only, because they combine non-dispatchable renewable energy sources (ND-RES) such as wind and solar PV with dispatchable renewable energy sources (D-RES) such as hydro and biomass, concentrated solar power (CSP) with thermal storage, and flexible demand (Oladimeji et al., 2021, Nemati et al., 14 Oct 2025). By contrast, the robust bidding framework of a renewable-only virtual power plant is formulated around renewable sources and flexible demand without thermal backup, and emphasizes that the portfolio must remain competitive in DAM, SRM, and IDM despite substantial uncertainty (Nemati et al., 2024).

This distinction matters because it changes what “flexibility” means. In strict RVPP formulations, flexibility is extracted from aggregation, demand adaptation, reserve-capable renewable operation, sequential market participation, and, in some cases, solar thermal storage internal to a renewable technology (Nemati et al., 2024). In broader renewable-based formulations, flexibility also comes from dispatchable renewables, especially hydro and biomass, which materially change schedulability, reserve provision, and uncertainty hedging (Nemati et al., 30 Jul 2025, Nemati et al., 14 Oct 2025). A recurrent misconception is therefore to equate all renewable-dominant VPPs with strict wind/PV-only portfolios; the literature does not support such an equivalence.

A second terminological axis concerns architectural emphasis. The Dynamic VPP (DVPP) frames a VPP as “a set of RES along with a set of control and operation procedures,” with local, global, and economic layers, and becomes substantively renewable-only when its chosen portfolio contains only RES units (Marinescu et al., 2021). The Building VPP (BVPP) instead emphasizes aggregation of building-side DERs through coordination between a BVPP EMS and autonomous BEMSs, and is therefore a renewable-dominant, mixed-flexibility building aggregation rather than a strict RVPP (Luo et al., 2020). These adjacent formulations broaden the RVPP concept from market scheduling to cyber-physical coordination and ancillary-service provision.

2. Portfolio composition and architectural patterns

The resource composition of RVPPs spans a spectrum from information-centric renewable aggregations to heterogeneous renewable portfolios. One line of work treats the VPP primarily as an information intermediary that aggregates uncertain renewable injections across subregions and provides forecasts to participating consumers rather than centrally dispatching assets (Chen et al., 2020). In that framework, the total renewable output of subregion $l$ is a stochastic injection $W_l$, market price is locally linearized as

$$\lambda(D,W)=\alpha\left(\sum_{l=1}^L\sum_{i\in\mathcal I_l}D_i-\sum_{l=1}^L W_l\right)+\beta_0,$$

and the VPP is explicitly “different from traditional retailer” because it “does not make profit directly from selling electricity but acts as a coordinator and provides supporting information/prediction” (Chen et al., 2020). This is RVPP-relevant because it isolates renewable uncertainty management as a standalone VPP service.

A second architectural pattern is the heterogeneous renewable market-participation model. The 12-node Spanish-style formulations include hydro, biomass, wind, solar PV, a solar thermal unit with storage, and flexible demands, all coordinated through a network-constrained unit commitment or dispatch framework (Oladimeji et al., 2021, Oladimeji et al., 2021). These models are not strict wind/PV-only RVPPs, but they are directly relevant because every named generation technology is renewable, and because they show how heterogeneity among renewable technologies can substitute for conventional balancing resources.

A third pattern is the building-side RVPP analogue. The BVPP architecture aggregates rooftop solar and building flexibility through a hierarchical EMS-BEMS structure in which the supervisory BVPP EMS performs global coordination, decision making, and risk evaluation, while each BEMS preserves local autonomy and translates VPP-level instructions into device-level schedules (Luo et al., 2020). This suggests that, in building-sector RVPPs, local autonomy and interoperability are structural design constraints rather than implementation details.

A fourth pattern is the converter-dominated control architecture of the DVPP. There the aggregation is conceived not merely as a market portfolio but as a dynamically coordinated RES plant spanning transmission and distribution domains, with local generator control, plant-level ancillary-service participation, internal redispatch, and interaction management with neighboring converter-based devices (Marinescu et al., 2021). A plausible implication is that strict RVPP research bifurcates into at least two partially overlapping agendas: market-oriented stochastic scheduling and control-oriented grid integration.

3. Market participation and scheduling formulations

RVPP scheduling is predominantly formulated around participation in sequential electricity markets. The most developed market structure in the cited literature is the Spanish-style sequence of DAM, SRM, and multiple IDMs, where energy bids are placed day-ahead, reserve capacity is sold in secondary reserve, and intraday sessions correct prior positions as renewable forecasts improve (Nemati et al., 2024, Oladimeji et al., 2021). In this class of models, DAM commitments become fixed inputs for SRM and later IDMs, and each IDM is an incremental correction rather than a full re-optimization of the day.

Two scheduling regimes recur. One is simultaneous DAM+SRM bidding, as in the worst-case profit model for an RVPP participating in simultaneous day-ahead and secondary reserve markets (Nemati et al., 2024). The other is sequential optimization with explicit anticipation of later markets, where the DAM decision internalizes future reserve and intraday opportunities, but accepted earlier commitments constrain later feasible actions (Nemati et al., 2024). The latter is particularly aligned with renewable-only portfolios because forecast quality improves as delivery approaches, making intraday recourse economically valuable.

At the optimization level, the objective is typically profit maximization from traded energy and reserve capacity, net of renewable operating costs, unit commitment costs for dispatchable renewable units, and compensation for flexible demand profile selection (Oladimeji et al., 2021, Nemati et al., 30 Jul 2025). In renewable-based formulations, joint power balance is enforced across energy and reserve activation states through compact reserve-state vectors, so that schedules remain feasible under upward activation, downward activation, and no activation (Nemati et al., 14 Oct 2025, Nemati et al., 30 Jul 2025). This means reserve provision is not an ex post add-on; it reshapes the feasible energy schedule.

Several formulations also show how internal network structure alters scheduling. The 12-node Spanish models embed nodal power balances and DC power flow constraints, while the reserve-capacity framework on a representative Swiss LV network uses linear DistFlow, voltage bounds, and branch apparent-power constraints, so reserve feasibility is filtered through the distribution network rather than treated as a copper-plate capability (Oladimeji et al., 2021, Zapparoli et al., 6 Oct 2025). The literature therefore rejects the idea that an RVPP’s tradable flexibility is merely the sum of DER nameplate ratings.

4. Flexibility mechanisms inside RVPPs

The main flexibility mechanisms documented in the literature are demand flexibility, renewable curtailment headroom, dispatchable renewable energy, renewable thermal storage, electrochemical storage or EV-like storage surrogates, and synthetic frequency support.

Demand flexibility is modeled with unusual richness in renewable-based VPP scheduling. The bi-level demand framework selects one pre-agreed profile in DAM,

$$p_{d,t}=\sum_{p\in\mathscr P} P_{d,p,t}u_{d,p},\qquad \sum_{p\in\mathscr P}u_{d,p}=1,$$

and then allows bounded continuous deviations around the chosen profile in IDM subject to ramp limits and minimum energy consumption (Oladimeji et al., 2021, Oladimeji et al., 2021). This structure turns demand into a market-coupled balancing resource that can absorb renewable forecast error and shape reserve capability without requiring direct appliance-level utility functions.

CSP with thermal storage plays a distinct role. In the heterogeneous renewable models, the solar thermal unit cannot be treated as either a purely dispatchable thermal unit or a purely non-dispatchable solar source because it combines stochastic solar-field input, thermal storage, startup losses, and piecewise-linear thermal-to-electric efficiency (Oladimeji et al., 2021). This makes CSP a renewable flexibility asset rather than just another generator. The RVPP-versus-ESS comparison similarly finds that CSP and hydro contribute substantially to aggregation value, while biomass contributes comparatively little because of small capacity (Nemati et al., 30 Jul 2025).

At the system-services layer, inverter-dominated RVPPs can be parameterized through virtual inertia and damping. The proposed frequency-support strategy for VPPs of inverter-based resources uses

$$\Delta P_\text{VPP} = -2H_\text{VPP}\frac{{\rm d}\Delta f}{{\rm d}t} - D_\text{VPP}(\Delta f+f_\text{DB1}),$$

and then minimizes reserve energy by exploiting the actual time-varying support trajectory rather than a fixed reserve block (Zhu et al., 10 Apr 2025). This is directly relevant to RVPPs composed of wind farms, solar panels with battery storage systems, and EV-like IBRs, because it shows how renewable-heavy portfolios can monetize seconds-scale active-power support without synchronous-machine inertia.

The literature also differentiates static feasibility from fast deliverable flexibility. The Feasible Operating Region (FOR) is the set of all feasible dispatch $PQ$ points of a VPP, whereas the Flexibility Operating Region (FXOR) is the subset reachable from a given dispatch point within a specified response time $\tau$ (Riaz et al., 2019). For RVPPs, this matters because a point may be feasible in aggregate but not reachable quickly enough for reserve delivery. The paper’s emphasis on converter-based fast resources, flexible load, and network topology is especially pertinent to renewable-dominated portfolios.

5. Forecasting, uncertainty, and coordination

Renewable forecasting and uncertainty handling are core RVPP functions rather than ancillary modeling details. In the decentralized renewable-prediction framework, each consumer receives a noisy local prediction

$W_{il}^{pre}=W_l+\varepsilon_{il},$

and uses the best linear estimator

$$\mathbb E[W_l\mid W_{il}^{pre}=w_{il}^{pre}]=A_{1i,l}+A_{2i,l}w_{il}^{pre}.$$

Prediction precision is

$$\tau_{il}:=\frac{\sigma_{W_l}^2}{\sigma_{W_l}^2+\sigma_{\varepsilon_{il}}^2}\in[0,1],$$

and the paper derives both a centralized benchmark and a decentralized prediction provision algorithm in which consumers buy only local forecasts while exchanging compressed signals with the VPP (Chen et al., 2020). The key welfare result is that prediction provision improves social total surplus, and the decentralized scheme produces a demand gap with zero expectation and bounded variance relative to the centralized benchmark (Chen et al., 2020).

Robust optimization is the dominant uncertainty-management paradigm in market-oriented RVPP research. The sequential robust bidding model for RVPPs in DAM, SRM, and IDMs explicitly handles asymmetric uncertainty in prices, ND-RES production, solar thermal output, and flexible demand by using median forecasts with separate positive and negative deviations, together with budgeted uncertainty over the whole horizon rather than hour-by-hour (Nemati et al., 2024). The simultaneous DAM+SRM robust bidding formulation likewise models worst-case profit under electricity-price, ND-RES, and flexible-demand uncertainty in a single-level robust mathematical approach (Nemati et al., 2024).

A more recent development is multi-bound robust optimization (MBRO) for quarter-hourly market participation, which distinguishes frequent moderate deviations from rare extreme ones through multiple uncertainty bounds and separate budgets (Nemati et al., 16 Feb 2026). This is important because classic single-bound robust optimization can be overly conservative when applied to renewable-heavy portfolios with many moderate forecast errors and relatively few extremes. Another line, focused on day-ahead offering, combines stochastic day-ahead price scenarios from a Markov process with budgeted robust PV uncertainty and reformulates the two-stage stochastic adaptive robust problem into a stochastic LP solved by a projected subgradient method (Meng et al., 2 Apr 2026). A plausible implication is that RVPP research increasingly treats price uncertainty probabilistically and renewable uncertainty robustly, rather than forcing both into a single uncertainty formalism.

Reserve qualification under uncertainty is also evolving beyond deterministic envelopes. The reserve-capacity framework with reliability and cost guarantees defines the maximum reliable reserve quantity $W_l$0 as a lower-tail quantile of the maximum feasible reserve $W_l$1, and uses subset simulation to estimate the extreme quantile efficiently (Zapparoli et al., 6 Oct 2025). This approach is not strict RVPP modeling, but it is methodologically transferable because renewable-only portfolios are especially sensitive to lower-tail scarcity events.

6. Empirical findings, comparative value, and limitations

The empirical literature consistently finds that internal flexibility, forecast-aware coordination, and robust scheduling materially increase RVPP value, but it also shows that the composition of the portfolio matters. In the 12-node RES-based VPP with bi-level demand flexibility, second-level intraday flexibility increases profit by up to 14% beyond the DAM objective on the clear-day case, and gains saturate beyond about 40% flexibility allowance (Oladimeji et al., 2021). In the heterogeneous renewable VPP with hydro, biomass, wind, PV, CSP, and flexible demand, coordination yields DAM/IDM profit improvements of 5% and 28% on a clear day, and 20% and 99% on a cloudy day (Oladimeji et al., 2021). These results directly support the view that aggregation value rises with renewable uncertainty.

Robust multi-market participation results point in the same direction. For the southern Spain case with hydro, biomass, CSP with thermal storage, PV, wind, and flexible demand, total profit falls from 11.38 k€ in the optimistic case to 3.37 k€ in the balanced case and -3.26 k€ in the pessimistic case, illustrating the cost of conservativeness under uncertainty (Nemati et al., 14 Oct 2025). Yet the same study shows that dispatchable renewables have the highest normalized contribution, flexible demand has the second-highest normalized contribution, and profit improves strongly as demand flexibility increases from 0% to 30% (Nemati et al., 14 Oct 2025). The RVPP-versus-ESS comparison sharpens this point: full RVPP aggregation produces additional profit over separate unit participation of 65.34 k€, 112.41 k€, and 144.49 k€ in favorable optimistic, balanced, and pessimistic cases, respectively, while matching that value with a standalone ESS requires 63 MWh, 124 MWh, and 177 MWh (Nemati et al., 30 Jul 2025). The paper also reports that removing flexible demand reduces additional profit by 78.5% under favorable optimistic conditions (Nemati et al., 30 Jul 2025).

Quarter-hourly modeling changes schedules materially. The multi-bound robust optimization paper reports normalized absolute differences between hourly and 15-minute schedules of 18.0–34.2% for DAM traded energy, 28.7–65.6% for upward secondary reserve, and 10.1–16.3% for downward reserve across the considered strategies, while MBRO increases profit by 24.9–49.2% relative to classic robust optimization (Nemati et al., 16 Feb 2026). On the reserve side, the minimal-reserve frequency-support strategy reduces reserve energy from 3.2 MWh to 1.54 MWh and reports a 51.88% improvement in financial gains relative to a peak-based fixed-reserve benchmark (Zhu et al., 10 Apr 2025). On the qualification side, the reserve-capacity framework finds a maximum reliable symmetrical reserve offer of 56.0 kW for the studied mixed-DER Swiss LV VPP, with average reserve costs between 0.34 CHF/kW and 0.40 CHF/kW, and shows that product requirements on reliability, duration, and ramp time strongly affect feasible reserve (Zapparoli et al., 6 Oct 2025).

Control-oriented and algorithmic results add a different dimension. The PV-storage day-ahead offering framework achieves roughly two orders of magnitude speedup over CC&G, specifically 122× to 204× depending on scenario count, while maintaining effectively identical objective values (Meng et al., 2 Apr 2026). In the EV-plus-renewables microgrid setting, the best RL controller keeps net load within $W_l$2 kW for 25,648 out of 35,041 timesteps, or 73.1% of the year, and in the tuned case raises self-consumption and autarky to 90.0% and 98.3% (Maldonato et al., 2024). These are not strict RVPP market results, but they indicate that control-layer design can materially increase renewable dispatchability proxies such as near-zero net load.

The limitations are equally consistent. Many influential models are renewable-based rather than strict renewable-only, because they include hydro, biomass, CSP with storage, ESS, flexible demand, EVs, or building flexibility (Oladimeji et al., 2021, Nemati et al., 14 Oct 2025). Several formulations are deterministic rather than stochastic or robust, and many omit balancing settlement, reserve activation chronology, or explicit network constraints (Oladimeji et al., 2021, Nemati et al., 2024). Some instantaneous flexibility frameworks neglect intertemporal storage constraints (Riaz et al., 2019). Others assume price-taking behavior, zero-price bids, or coarse battery degradation models (Nemati et al., 14 Oct 2025, Meng et al., 2 Apr 2026). The literature therefore supports a precise conclusion: RVPPs are a viable research and operational category, but their demonstrated competitiveness depends strongly on how strictly “renewable-only” is interpreted, on whether flexible demand and dispatchable renewable resources are present, and on whether uncertainty, network feasibility, and market sequencing are modeled explicitly.