On August 30, 2024, the Kenyan power grid suffered a blackout of unusual proportions, plunging the capital Nairobi and six of the eight regions on the national grid into darkness. This phenomenon has only grown increasingly familiar in the East African country as it ended 2023 experiencing three nationwide blackouts in four months.
Kenya is facing the possibility of scheduled power outages, known as load shedding, in the near future. Load shedding is a strategy to balance the available power with the demand, ensuring a more stable and reliable electricity supply for everyone. It involves cutting off power to certain areas or sectors for a specified period of time, usually on a rotational basis.
Load shedding, the deliberate shutdown of electricity in parts of a power distribution network to prevent the collapse of the entire system, has become a daily challenge for many East Africans. This issue is driven by multiple factors: aging infrastructure being unable to keep up with rapid urbanisation, financial constraints within the energy supply companies, and climate change-induced droughts diminishing the capabilities of hydroelectric power generation.
But what exactly is load shedding? How does it work? Load shedding is a controlled process where electricity supply is intentionally turned off on parts of the grid to prevent the entire system from collapsing. In essence, the national grid can become unstable if there is insufficient supply to meet consumer demand. When Eskom can’t generate enough electricity to meet national demand (due to breakdowns, maintenance, or limited fuel), it sheds “load” to avoid total blackout.
Understanding its causes and consequences is step one. As renewable energy gains momentum, there is a future where “load shedding” becomes a relic of the past.
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Causes of Load Shedding in Kenya
Several factors contribute to the frequent power outages in Kenya:
- Overloading the Grid: When the demand for electricity surpasses the capacity the grid can handle, it leads to overloading.
- Equipment Failure: Aging infrastructure or sudden equipment breakdowns, like transformers or power lines, can cause widespread power failures.
- Neglect of Transmission Network: Kenya has also, for years, neglected to invest in its transmission network.
National blackouts often stem from cascading failures, a series of interdependent component breakdowns that gradually weaken the entire power system. As these nearby components struggle to manage the heightened load, they too succumb to the strain and fail, exacerbating the situation.
Additionally, the Kenya-Tanzania Power Interconnection originates from the Isinya substation, traversing through Arusha before reaching Singinda in Tanzania. Kenya’s transmission network predominantly relies on radial lines, with 132kV voltage predominating.
In terms of generation capacity, Kenya boasts a total installed capacity of approximately 3,300 megawatts. All these factors mean that the actual generation from this installed capacity is much lower. The country does not have excess power. This includes 200MW imported from Ethiopia and 210.3MW of solar, which is produced locally but is unavailable at night.
Radial transmission lines, while simpler in design, can pose challenges in terms of reliability and redundancy. If there’s a failure or outage at any point along the line, it can lead to widespread power loss as the flow of electricity is dependent on that single source.
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Low transmission voltages, such as those at 132kV, can limit the efficiency and capacity of the transmission network. Low-voltage transmission lines can suffer from higher resistance, resulting in greater energy dissipation as heat and reducing the amount of usable electricity reaching its destination. When there’s a surge in demand, like during extreme weather or peak usage times, these lines can get overloaded.
To prevent further national blackouts resulting from cascading failures, the government has taken a strategic step: directing the islanding of the grid from the western region. Islanding refers to the deliberate isolation of a section or region within an electrical grid. In this context, isolating the grid from the western region involves creating an independent or ‘islanded’ section of the grid that operates autonomously.
This measure serves as a protective barrier against cascading failures. By isolating the western region, any faults or failures occurring in this area will not propagate and trigger widespread outages across the entire grid.
In a similar incident in Turkey in 2015, a national blackout occurred due to maintenance on a critical East-West power line. The situation in Turkey was complex, involving factors like long-distance power transmission, maintenance issues, and insufficient understanding of how to handle drastic changes in power flow.
The recent national blackout in Kenya might have been preventable to some extent. Preventing overloading necessitates a thorough understanding of the grid’s load characteristics and the implementation of protective systems.
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Preventive Measures
To prevent further national blackouts, several measures can be implemented:
- Continuous Load Monitoring: Implement systems that continuously monitor transmission line loads. Advanced monitoring technologies coupled with real-time data analysis can predict and identify potential overloads before they occur.
- Load shedding and system balancing: Load shedding is the controlled reduction of power demand during critical situations. Implementing load shedding schemes helps prevent overloading of transmission lines by reducing the overall demand on the system.
- Redundancy and Backup Plans: Build redundancy into the grid system by creating alternative pathways for power distribution.
- Protective relay coordination: Proper coordination of protective relays and circuit breakers is essential to isolate faults and overloads swiftly. Ensuring that protective devices are appropriately set, calibrated, and coordinated helps localize faults and minimize the spread of failures.
- Enhanced monitoring and situational awareness: Real-time monitoring and advanced control systems provide operators with accurate and timely information about the state of the power system. Monitoring includes measurements of power flows, voltage levels, and system dynamics.
Preventing cascading failures requires a comprehensive and multi-layered approach that combines system planning, real-time monitoring, protective measures, and swift response strategies.
Impact of Frequent Power Outages
These blackouts have significant consequences on businesses:
- Small businesses are hit the hardest.
- When businesses can’t operate, they cut staff.
- Companies and households now spend thousands on inverters, solar panels, UPS systems, or diesel generators.
Load shedding also has a negative impact on the productivity and profitability of various sectors of the economy, especially the manufacturing, mining, agriculture and service industries. These sectors rely heavily on electricity to operate machinery, equipment, computers and other devices. Load shedding also affects the competitiveness of Kenyan businesses in the regional and global markets.
The economic consequences are severe, with the World Bank estimating that power outages could cost countries in the region up to 5% of their total GDP.
Load shedding also has a detrimental effect on the quality of life and well-being of Kenyans. It disrupts the normal functioning of households, schools, hospitals, public services and other essential facilities. It affects the provision of health care, education, water, sanitation, security and communication. It exposes people to health and safety risks, such as food spoilage, water contamination, fire hazards, crime and violence.
Load shedding also exacerbates the existing inequalities and vulnerabilities in the society. It affects the poor and marginalized groups more than the rich and privileged ones. It widens the gap between the urban and rural areas, where access to electricity is already unequal. It also increases the gender disparities, as women and girls bear the brunt of the domestic chores and responsibilities that require electricity, such as cooking, cleaning, washing and ironing.
Load shedding also has an adverse impact on the environment and the climate. It encourages the use of alternative sources of energy, such as diesel generators, kerosene lamps, charcoal stoves and firewood. These sources are not only expensive and inefficient, but also emit harmful pollutants and greenhouse gases that contribute to air pollution, respiratory diseases, deforestation, desertification and global warming. Load shedding also undermines the efforts to transition to a green and low-carbon economy.
Impact on Education and Public Health
- Students lose out on instruction time, especially in under-resourced areas without backup power. Online learning is disrupted, and exam schedules are adjusted.
- Public health facilities are not immune.
- Prolonged darkness increases burglary, especially in low-income areas.
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What is the Government Doing?
The government has taken several steps in recent years to address the challenges. For instance, the State has contracted Africa 50 to build the 165km 400kV Losuk-Lessos and 72km 220kV Kisumu-Musaga lines for Sh44 billion.
Kenya has made significant strides in developing and harnessing renewable energy sources, such as geothermal, hydro, wind and solar. These sources are clean, cheap, abundant and renewable. They have the potential to meet the growing demand for electricity and reduce the dependence on fossil fuels and imports. However, load shedding reduces the incentives and returns for investing in renewable energy projects and infrastructure.
Load shedding is not inevitable or irreversible. It can be avoided or minimized by taking proactive and preventive measures to address the underlying causes and challenges of the power sector.
- Expanding and upgrading the power transmission and distribution network to increase its capacity, efficiency and reliability.
- Enhancing the maintenance and management of the existing power plants and equipment to improve their performance, availability and lifespan.
- Accelerating the completion and commissioning of the ongoing and planned power projects, especially the coal-fired plants of Medupi and Kusile, which are expected to add 9,564 MW of capacity to the grid.
- Promoting the development and integration of renewable energy sources, such as geothermal, hydro, wind and solar, into the national grid.
- Implementing the reforms and recommendations of the Presidential Taskforce on Independent Power Producers, which was chaired by John Ngumi.
- Encouraging the participation and involvement of the private sector, the civil society and the consumers in the power sector.
Consumer Coping Mechanisms
Load shedding isn’t just a nuisance.
- Companies and households now spend thousands on inverters, solar panels, UPS systems, or diesel generators.
Machine Learning for Energy Demand Forecasting
Machine learning-based energy demand forecasting and load profiling present solutions to reduce the impact of load shedding by enabling more accurate prediction and management of electricity demand.
Energy Demand Forecasting In East Africa, energy demand forecasting has not been prioritised. The reasons for this include a lack of advanced technology that can be used for accurate demand forecasting alongside budgetary limitations that make it hard to allocate funds for it. Consequently, this leads to lack of high-quality historical data, making it challenging to conduct accurate forecasts.
Energy demand forecasting is the process of predicting future energy demand based on the historical data provided. Energy suppliers use energy forecasting to predict the quantity that needs to be generated to ensure that there is efficient energy distribution. Incorporating the ever-changing consumer patterns into distribution is crucial, to ensure that each household and firm has access to sufficient energy to operate daily.
Load Profiling Load profiling is the process of analysing energy consumption patterns of a specific market over time to understand when energy is being consumed. This method creates a detailed model of electricity usage patterns for various consumers, such as households and firms. By identifying high demand areas, utility companies can prioritise regions for infrastructure upgrades and targeted demand response programs. Effective demand forecasting allows utility service providers to optimise operations, enhance grid stability and improve customer satisfaction through efficient energy management.
A load profile is a graph that illustrates the variation in electricity usage over time derived from data collected at regular intervals. This data is analysed to identify consumption patterns, categorize different sectors, and understand demand variations with pricing models. The insights gained from these profiles are also used to forecast future energy consumption trends. The graph helps grid operators understand the consumption patterns within different sectors through highlighting the daily peak and off-peak consumption times.
Short term load forecasting is the process of predicting the demand over a short time. These forecasts enable grid operators to make real-time decisions regarding the power distribution and avoid sudden outages. Moreover, the utility can optimise the usage of available resources in advance, to ensure that enough generation capacity is available to meet the demand.
How Load Profiling is Done in East Africa:
- Smart and prepaid meters - The installation of meters has rapidly increased within households to be most common method in East Africa. These meters provide real-time data on electricity usage and can take readings in set time intervals, allowing utility companies to have a detailed load profile. Moreover, pre-paid metering is a system where the consumer purchases electricity tokens in advance, enabling energy suppliers to anticipate how much energy will be consumed by each household.
- Manual meter reading - In areas where smart meters are not readily available, suppliers have utility workers that visit households and record consumption data. Despite its labour-intensive nature, it is still a widely used method in East Africa.
- Data loggers - Some utility companies use data loggers that are installed at key points in the distribution network to monitor electricity flows - which correlates to its consumption.
East Africa is filled with an extensive amount of untapped energy resources. However, only 50% of the total population has access to electricity services, with rural areas having the least exposure at around 30%. This is due to inadequate financial resources to connect the sparse population in rural areas to the national electric grid.
Even where off-grid solar systems are prioritised in the rural areas, there are often inadequate resources available to cater to each household. Additionally, some of the products are too expensive for consumers, given their low disposable income. Nonetheless, East Africa’s energy supply has a lot of potential in the future due to its vast renewable energy reserves.
With the prioritisation of increasing renewable energy supply, the power market in East Africa is predicted to grow at a compound annual growth rate (CAGR) of 3% between 2021 and 2026. Exploring renewable energy sources more intentionally could hold the key to reducing load shedding as it diversifies the power supply portfolio.
Small scale energy projects such as wind and solar are currently providing access to electricity in remote and rural areas
Leveraging Machine Learning for Energy Demand Forecasting Energy demand forecasting can utilize statistical methods like a measure of the autoregressive integrated moving average (ARIMA) for short-term predictions based on past consumption patterns, while regression analysis incorporates various external factors such as temperature and time of day for more accurate results.
Combining these traditional methods with machine learning (ML) techniques, like artificial neural networks (ANN) and support vector machines (SVM), enhances accuracy by modelling complex, non-linear relationships in energy consumption - making it possible to better anticipate and manage future demand.
The most effective method for short-term forecasting is likely an ARIMA prediction model, given that historical data shows strong patterns, due to its simplicity and interpretability. For incorporating external factors, regression analysis is preferable, as it can handle multiple input variables and easily extrapolate future demand. SVM models are suited for complex, non-linear relationships, but they require expert interpretation.
Anomaly Detection and Outlier Handling
Anomaly detection traditionally relied on manual data inspection by experts, but the increasing volume of data has made this method impractical. Consequently, automatic anomaly detection using machine learning techniques such as one class support vector machine (OCSVM), has become more prevalent.
OCSVM identifies outliers by learning the distribution of a dataset, detecting data points that significantly deviate. This approach helps grid operators detect unusual energy consumption patterns, understand their causes, and prepare for disruptions, thus preventing unplanned power cuts due to load shedding.
Key factors leading to anomalies in energy consumption would be:
- Social and cultural events - Major events like the football World Cup, public holidays, and music festivals can significantly boost energy consumption due to increased live streaming, decorations, and usage of lighting and sound systems.
- Economic activities - Economic booms raise energy usage through heightened industrial activity, while recessions often reduce it. Production cycle changes also impact consumption, with lower usage during planning stages.
- Environmental factors - Extreme weather, such as heatwaves or cold snaps, can cause unexpected spikes or drops in energy demand.
Challenges and Opportunities
The potential for the application of machine learning models to account for unique local factors is large. This nuanced approach to forecasting allows for more efficient resource allocation and better infrastructure planning, as it leverages region-specific insights to make accurate predictions. By effectively integrating diverse and localized data sources, ML models offer a significant advantage over traditional methods, ensuring that energy supply can be more reliably aligned with consumer demand.
Implementing machine learning models faces several challenges:
- Limited access to comprehensive and accurate data can impair ML model performance.
- Insufficient internet and computational resources hinder ML development and installation.
- A lack of trained data scientists and experts complicates system development and maintenance.
- High expenses for ML technology can be prohibitive for smaller organisations.
- Frequent power outages and unreliable internet affect data collection and model reliability.
South Africa's Experience with Load Shedding
Kenya can learn from the experience and example of South Africa, which has been grappling with load shedding for more than a decade. The main causes of load shedding in South Africa are similar to those in Kenya: ageing infrastructure, poor maintenance, corruption, mismanagement and sabotage. The consequences are also similar: economic losses, social disruptions, environmental damages and political instability.
However, South Africa also offers some unique insights and lessons for Kenya. One of them is the importance of diversifying the power sector and reducing the monopoly and dominance of Eskom, the state-owned utility that generates, transmits and distributes electricity in the country. Another lesson is the need to balance the social and environmental objectives of the power sector with the economic and financial realities.
South Africa is not standing still.
- Eskom is investing in large-scale battery energy storage systems (BESS).
- Private solar and wind farms are now allowed to sell electricity directly to the grid.
- More homeowners and businesses are installing solar.
- If you can’t beat it, plan around it. Foreign investors view power stability as a basic requirement.
The World Bank has already downgraded growth forecasts, citing energy insecurity as a key bottleneck.
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