A Comprehensive Review on the Integration of Renewable Energy Through Advanced Planning and Optimization Techniques
Abstract
1. Introduction
2. Traditional Power System Planning Methodologies
2.1. Description
- Year 1 (base year). Electricity demand corresponds to present-day levels, supplied mainly by the existing hydro and thermoelectric power plants.
- Year 10 (medium-term). Demand grows, prompting the first wave of utility-scale wind and solar additions that complement, rather than replace, legacy hydro and thermal units.
- Year 20 (long-term). The system reaches a fully diversified portfolio—wind, solar PV, hydro, and battery energy storage systems—while thermal generation is progressively retired; this mix provides the flexibility and zero-carbon supply needed to meet higher residential, commercial, and industrial loads.
2.2. Limitations of Deterministic Planning Under High VRE
- Rigid Structure: The fundamental need for rapid-response, flexible supply-side resources such as energy storage, demand response, as well as other resources, is neglected [10].
3. Challenges of Renewable Energy Integration
3.1. Intermittency and Variability
3.2. System Flexibility Requirements
3.3. Stability, Reliability, and Congestion
3.4. Economic and Market Implications
4. Advances in Stochastic Planning and Optimization
4.1. Stochastic Planning
4.2. Overview of Optimization Methods and AI Support Tools
- (a)
- Mixed-Integer Linear Programming (MILP). MILP remains the dominant exact method because it can co-optimize binary siting decisions and continuous power-flow variables within a single framework. Applications range from voltage regulator siting and loss minimization [22] to joint placement of PV, wind, and battery energy storage systems [23,24] and long-term transmission expansion studies that weigh capital and operating costs [25,26]. Commercial and open-source solvers guarantee global optimality for moderate-sized instances, but computational burdens rise rapidly with network scale and scenario count.
- (b)
- Meta-heuristic Algorithms. Bio-inspired techniques such as genetic algorithms (GA), particle swarm optimization (PSO), and ant colony optimization (ACO) excel in large, non-linear, multi-objective search spaces where exact solvers struggle [27]. GA has improved PV maximum power point tracking under partial shading [28] and reduced operational costs in hybrid renewable systems [29]. PSO delivers fast convergence in power flow optimization for grids with high wind and solar penetration [30], and hybrid PSO variants help avoid local minima [31]. ACO has proven effective for routing new transmission lines and allocating generation in smart grid settings with growing renewable shares [32,33].
- (c)
- Machine-Learning/AI-assisted Methods. Data-driven models complement optimization by improving forecasts of renewable generation and electricity demand [34]. Accurate predictions allow planners to better align variable supply with consumption patterns [35], thereby reducing reliance on costly backup resources. ML-enhanced load forecasting has already helped minimize curtailment and lower operational costs in systems with high PV and wind shares [36]. Beyond forecasting, AI surrogates can emulate complex network constraints, accelerating large-scale stochastic optimization.
4.3. Mathematical Formulations
- Notation:
- Sets
- –
- —generation or storage sites
- –
- —time steps
- –
- —stochastic scenarios
- Parameters
- –
- —generation cost
- –
- —storage cost
- –
- —investment cost
- –
- —demand at time t
- –
- —probability of scenario s
- –
- —capacity limit of unit i
- Decision variables
- –
- —power dispatch
- –
- —stored energy
- –
- —binary build decision
4.3.1. Deterministic MILP Model
4.3.2. Two-Stage Stochastic MILP
4.3.3. Meta-Heuristic Objective Function
4.3.4. ML-Based Forecasting Loss
5. Energy Storage Integration
5.1. Concepts for Storage Technologies
- Lithium-Ion Batteries: Among currently existing storage technologies, lithium-ion is the most common implementation, especially in grid-scale applications and in distributed energy systems. Due to their high energy density, fast response times, and decreasing cost, they remain the first choice for short-term energetic reserve, frequency regulation, and smoothing the variability of renewable generation. The utilization of lithium-ion batteries in residential and utility-scale applications make them integral for balancing supply and demand in renewable-heavy grids [45]. Despite this, they raise an issue of degradation over time and environmental issues due to the mining and disposal of lithium [46].
- Pumped Hydro Storage (PHS): Pumped hydro storage continues to be the most commonly deployed high-capacity energy storage technology globally and remains the most prevalent form of energy storage in the world. During periods of low demand, it uses gravitational energy to pump water to a higher elevation and then releases it to generate electricity during peak demand times. PHS offers particularly high reliability for backup power during high-demand periods and long-term storage capacity. Due to its high efficiency and reliability, PHS is well suited for enhancing grid stability and energy arbitrage; however, its applicability is restricted by geographic limits and high initial capital costs [47,48].
- Flow Batteries: These batteries, with separate electrolytes from reaction cells, offer a number of advantages over lithium-ion technology for medium- and long-term energy storage. They provide extended discharge durations, making them suitable for long-term constant energy supply applications. Flow batteries are very effective at integrating renewable energy because they have the ability to store energy for a long time while also delivering power over periods of several hours to several days. Moreover, they offer a long power cycle lifespan and are significantly more effective for frequent cycling at the cost of a low energy density when compared to lithium-ion batteries [49,50].
5.2. Joint Planning Models for Generation, Transmission, and Storage
- Z is the total system cost to minimize,
- is the cost of generating power from unit i,
- is the power generated by unit i at time t,
- is the cost of transmission expansion,
- is the power transmitted at time t,
- is the investment cost for expanding generation unit i,
- is a binary variable representing whether new generation or transmission capacity is built at unit i,
- is the cost of energy storage,
- is the energy stored or released by the storage system at time t.
- is the investment cost of BESS (battery energy storage systems),
- is the network operation cost,
- is the cost of power purchased from the grid,
- is the cost of network losses,
- is the cost of maximum demand or peak demand charge.
5.3. Long-Duration and Seasonal Storage
6. Regulatory and Market Challenges
6.1. Regulatory Framework
- Renewable Portfolio Standards (RPS): The utilities are mandated, under RPS, to source some percentage of electricity from renewable sources. This results in a direct market for renewable energy projects and always generates a long-term demand for renewables. Several regions have started implementing RPS policies, pushing utilities to gradually decrease their dependence on fossil fuels and replace them with renewable energy [59,60].
- Feed-In Tariffs (FiTs): FiTs guarantee long-term contracts with payment rates based on renewable energy production. They reduce market risks and give predictable returns to incentivize investment in renewable energy projects. FiTs have, however, proven to vary by region and have been subject to constant political and economic adjustments, affecting the long-term stability of the renewable energy market [60,61].
- Carbon Pricing Mechanisms: Policies such as carbon taxes and cap-and-trade systems require utilities and industries to price their carbon footprints. These mechanisms, by raising the cost of carbon-intensive energy sources, create a market-driven incentive for cleaner alternatives, indirectly giving renewables a competitive advantage and encouraging their integration into power systems [62].
6.2. Market Mechanisms
- Dynamic Pricing: Traditional electricity markets do not employ fixed pricing structures based on actual real-time supply and demand. Since solar and wind power vary over the course of a day, dynamic pricing approaches, such as time of use rates and real-time pricing (RTP), can offer consumers an incentive to shift their demand in the face of, or according to, levels of renewable generation. These models have been designed to promote consumption during periods of high renewable generation and reduce demands during periods of low renewable supply [60,64].
- Capacity Markets: These markets help to guarantee an adequate reserve generation capacity to meet peak demand even at times when renewable energy production is low. They pay generators to be available to supply when needed (grid reliability) and when they cannot supply (low renewable generation). Demand response and energy storage, which are proven to be crucial to deal with renewable energy variability, are also integrated into capacity markets [62,65,66].
- Ancillary Services Markets: Greater renewable energy penetration increases the need for ancillary services such as frequency regulation, voltage control, and spinning reserves to keep the system stable. Renewable generators and energy storage systems can be compensated for providing these critical services through markets for ancillary services, depending on the regulations of each country [67].
7. Open Challenges and Future Directions
7.1. Future Research Directions
- The growing complexity and uncertainty associated with renewable energy integration call for more sophisticated planning models. While mixed-integer linear programming (MILP) remains a widely used optimization method, it must be complemented by hybrid algorithms that incorporate meta-heuristics, machine learning, and artificial intelligence. These advanced approaches are well-suited for solving multi-objective optimization problems, balancing trade-offs between cost, grid reliability, and environmental impact. In particular, the development of algorithms that improve the accuracy of renewable generation forecasting and optimize network reinforcements while considering the variability of renewables is essential for effective power system expansion planning [68,69,70].
- Improving Long-Term Climate and Load Forecasting: Planning the expansion of power systems requires highly accurate forecasts. The lessons learned from these case studies will help enhance weather forecasting models for renewable energy production—especially wind and solar PV, and enable their integration into power system planning tools. This issue is approached from the perspective of long-term load forecasting, accounting for demand shifts driven by electrification trends (e.g., electric vehicles). With these improved forecasting capabilities, system planners will be better equipped to anticipate when and where capacity expansions are necessary [71,72].
- Incorporating Energy Storage in Expansion Models: To deal with the intermittency of renewable sources, research on the role of energy storage in expansion planning is of cardinal importance. More advanced models are needed to appropriately size and locate renewable generation in conjunction with storage systems. These models should include the entire lifecycle of storage technologies, including degradation, cost evolution, and operational constraints [73,74].
- Resilience in Planning for Extreme Events: As global warming becomes more apparent, power systems must be able to withstand the growing impacts of climate change, including hurricanes and wildfires. In future research, resilience metrics should be integrated into expansion planning models so that new infrastructure, including renewable generation and storage, can continue to serve its customers through these disruptive events [75,76].
7.2. Emerging Trends
- Blockchain for Energy System Transactions and Management: As a result, the articles [77,78] discuss the potential use of blockchain technology to decentralize energy markets and make the energy system more efficient. For expansion planning blockchain can enable peer-to-peer (P2P) energy trading whereby consumers can sell PV energy to others. Reduction in dependence on centralized generation and transmission assets could change traditional grid planning.
- AI and Digitalization in Expansion Planning: In this way, grid expansion decisions are being revolutionized based on their use of AI and digital twins. Digital twins, virtual replicas of physical power systems, allow planners to simulate how the integration of new renewable assets, storage systems, or transmission lines affects a power system by making costly investments. In addition, AI-driven models can optimize expansion through supervised learning from historical data and unsupervised learning to predict future grid behaviors under different scenarios. Creating more flexible and adaptive power grids that can further adapt to dynamic conditions introduced by renewables is essential with this technology [79,80].
- Sector Coupling and Integrated Energy Systems: Expansion planning now also focuses on the concept of sector coupling, connecting the electricity sector with the other energy sectors, such as heating and transport. Planners can design more integrated and flexible energy systems by using renewable electricity to serve other sectors, for example, by using surplus solar power for heating or hydrogen production. In order to continue along this track, the grid will need to expand its infrastructure to support electrification in different sectors, thereby raising yet another need for careful and concerted expansion planning [81,82,83].
- Future Market Reforms and Flexible Pricing Mechanisms: To anticipate this, electricity markets must develop to accommodate more flexible and dynamic operations as variable renewable penetration increases. New market structures such as capacity markets, demand response programs, and time-of-use rates will have to be accounted for in future grid expansion planning. By institutionalizing these reforms, incentives will be provided for distributed generation and storage, which will reduce the need to expand the underlying large-scale grids at a tremendous cost [60,84].
8. Discussion
8.1. Mathematical Formulations in Renewable-Energy Planning
8.2. Challenges in Expanding Renewable Energy Systems
8.3. Future Research Directions
9. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
ACO | Ant colony optimization |
AI | Artificial intelligence |
BESS | Battery energy storage system |
CAES | Compressed air energy storage |
DG | Distributed generation |
GEP | Generation expansion planning |
FiT | Feed-in tariffs |
LVRT | Low-voltage ride through |
MILP | Mixed-integer linear programming |
ML | Machine learning |
MOEA | Multi-objective evolutionary algorithms |
P2P | Peer-to-peer |
PHS | Pumped hydro storage |
PSO | Particle swarm optimization |
PV | Photovoltaic |
RPS | Renewable portfolio standards |
RTP | Real-time pricing |
TEP | Transmission expansion planning |
VRE | Variable renewable energy |
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Challenge | Description | Representative Solutions |
---|---|---|
Intermittency & Variability | Rapid, weather-driven output fluctuations. | Short-term storage, advanced forecasting, curtailment management. |
Flexibility Needs | Demand for fast balancing resources grows with VRE share. | BESS, pumped hydro, demand response, multi-energy systems. |
Stability & Reliability | Reduced inertia, voltage/frequency excursions, congestion. | Synthetic inertia, LVRT controls, dynamic line rating, targeted reinforcements. |
Economic Impacts | Underestimated costs of reserves, cycling, curtailment. | Integrated optimization of VRE, storage, and backup; market mechanisms. |
Method | Opt. Guarantee | Scalability ∗ | Runtime | Key Strengths/Limitations | Key Refs. |
---|---|---|---|---|---|
MILP | Global (gap ≤0.1%) | ≲105 vars | min–h | + Exact, rich modelling; mature solvers − Exponential growth with binaries/scenarios; licence cost | [22,25] |
GA (heuristic) | 1–5% gap | vars | min–h | + Robust global search; multi-objective ready − Parameter tuning; premature convergence risk | [27,29] |
PSO (heuristic) | 1–3% gap | vars | s–min | + Very fast; simple implementation − Local-optima susceptibility; inertia sensitivity | [30,31] |
ACO (heuristic) | 1–4% gap | vars | min–h | + Good for routing/discrete siting − Slower than PSO; many control parameters | [32,33] |
ML/AI support | — | ≥107 samples | ms–s (inf.) | + High-fidelity forecasts; surrogate constraints speed optimization − Requires large datasets; no optimality guarantee itself | [34,35] |
Method | Study/System | Decision Problem | Key Outcome |
---|---|---|---|
Deterministic MILP | Huang et al. (2023) [2] —Three-area Chinese grid | Co-optimization of wind, PV, and dual storage (PHS/CAES & BES) | Met 2030 and 2060 targets with wind/PV curtailment below 5%/3% |
Two-stage stochastic MILP | Micheli et al. (2020) [38] —Italian grid (663 buses) | Gen + TX expansion under fuel/CO2 price uncertainty | Expected cost €404, 0.02 optimality gap; all reliability criteria met |
Heuristics | Keokhoungning et al. (2022) [39] —IEEE-118 & Lao PDR | Transmission expansion with PV, wind, and battery siting | Maintained N-1 reliability, reduced losses,s and deferred corridors in ≈90 s |
Challenge | Description | Proposed Solutions |
---|---|---|
Variability and Intermittency | Unpredictable output from solar and wind | Energy storage and demand-side management |
Grid Stability | Lack of inertia in renewables | Synthetic inertia and enhanced grid flexibility |
Economic Optimization | High costs of storage and renewables integration | Advanced optimization techniques and cost reduction |
Research Area | Description | Expected Impact |
---|---|---|
Advanced Optimization Algorithms | Development of more robust algorithms for planning | Increased efficiency in system planning |
Climate Modeling Improvements | Integration of more accurate climate models in planning | Enhanced accuracy in renewable forecasting |
Energy Storage Innovations | Development of cost-effective storage technologies | Greater reliability and grid resilience |
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Barrera-Singaña, C.; Comech, M.P.; Arcos, H. A Comprehensive Review on the Integration of Renewable Energy Through Advanced Planning and Optimization Techniques. Energies 2025, 18, 2961. https://doi.org/10.3390/en18112961
Barrera-Singaña C, Comech MP, Arcos H. A Comprehensive Review on the Integration of Renewable Energy Through Advanced Planning and Optimization Techniques. Energies. 2025; 18(11):2961. https://doi.org/10.3390/en18112961
Chicago/Turabian StyleBarrera-Singaña, Carlos, María Paz Comech, and Hugo Arcos. 2025. "A Comprehensive Review on the Integration of Renewable Energy Through Advanced Planning and Optimization Techniques" Energies 18, no. 11: 2961. https://doi.org/10.3390/en18112961
APA StyleBarrera-Singaña, C., Comech, M. P., & Arcos, H. (2025). A Comprehensive Review on the Integration of Renewable Energy Through Advanced Planning and Optimization Techniques. Energies, 18(11), 2961. https://doi.org/10.3390/en18112961