A screen displays the logo of Spotify on the floor at the New York Stock Exchange on Dec. 4, 2023.
Brendan Mcdermid | Reuters
Spotify will raise prices as it invests in new features and targets 1 billion users, the Financial Times reported on Sunday citing the music streaming provider’s Co-President and Chief Business Officer Alex Norstrom.
The increases would be accompanied by planned new services and features, the FT cited Norstrom as saying in an interview.
Spotify did not immediately respond to a Reuters request for comment.
Earlier in August, the Swedish firm said it would increase the monthly price of its premium individual subscription in some markets from September, as it looks to improve profit margins.
It said the price will rise to 11.99 euros ($14.05) from 10.99 euros in markets including South Asia, the Middle East, Africa, Europe, Latin America and the Asia-Pacific region.
“Price increases and price adjustments and so on, that’s part of our business toolbox and we’ll do it when it makes sense,” Norstrom told the newspaper.
Price increases combined with cost-cutting efforts in recent years helped Spotify achieve its first annual profit last year.
KABUL, Aug. 24 (Xinhua) — Afghanistan’s central bank Da Afghanistan Bank announced a 21 percent rise in the value of the afghani against foreign currencies, notably the U.S. dollar, over the past four years, crediting strategic monetary policies and expanded global banking ties, local media outlet TOLOnews reported Sunday.
“Our effort is to maintain the Afghani’s stability in a better way and not allow severe fluctuations to occur in this regard,” the media quoted Hasibullah Noori, the bank’s spokesman, as saying.
According to the official, the achievement is attributed to effective monetary policies aimed at stabilizing the nation’s economy, strengthening the banking sector, and enhancing financial support systems.
These efforts are part of a broader strategy to rebuild Afghanistan’s economy. ■
From American Eagle to Swatch, brands appear to be making a lot of blunders lately.
When actress Sydney Sweeney’s jeans campaign came out last month, critics lambasted the wordplay of good “jeans” and “genes” as tone deaf with nefarious undertones.
More recently, an advert from Swiss watchmaker Swatch sparked backlash for featuring an Asian model pulling the corners of his eyes, in an offensive gesture.
Colgate-Palmolive‘s ad for Sanex shower gel was banned in the U.K. for problematic suggestions about Black and white skin tones. And consumers derided Cracker Barrel’s decision to ditch its overalls-clad character for a more simplistic text-based logo as “sterile,” “soulless,” and “woke.”
Meanwhile, recent product launches from Adidas and Prada have raised allegations of cultural appropriation.
That has reignited the debate about when an ad campaign is effective and when it’s just plain offensive, as companies confront increased consumer scrutiny.
Outdated playbooks
“Each brand had its own blind spot,” David Brier, brand specialist and author of “Brand intervention” and “Rich brand, poor brand” told CNBC via email.
He noted, however, that too many brands are attempting to respond to consumers with an outdated playbook.
“Modern brands are trying to navigate cultural complexity with corporate simplicity. They’re using 1950s boardroom thinking to solve 2025 human problems,” he continued.
“These aren’t sensitivity failures. They’re empathy failures. They viewed culture as something to navigate around rather than understand deeply.”
The new Cracker Barrel logo is seen on a menu inside the restaurant on Aug. 21, 2025 in Homestead, Florida.
Joe Raedle | Getty Images
Some companies have had success in tapping into the zeitgeist — and, in some cases, seizing on other brands’ shortcomings.
Gap, for instance, this week sought to counter backlash against Sweeney’s advertisement with a campaign in which pop group Katseye lead a diverse group of dancers performing in denim against a white backdrop.
Brier said companies should consider how they can genuinely connect with consumers and be representative, rather than simply trying to avoid offense.
“No brand can afford to fake understanding. No brand can ‘committee its way’ to connection. No brand can focus-group its way to authenticity. In 2025, customers can smell the difference from a mile away,” he added.
Balancing the risk
Nevertheless, ads are meant to spark conversation, and at a time when grabbing and maintaining consumers’ attention — and share of wallet — is increasingly difficult, brands have a fine balance to tread.
“Brands live and die by standing out and grabbing attention. On top of that, iconic and culturally relevant brands want to stand for something and be recognized for it. Those are tough asks,” Jonathan A.J. Wilson, professor of brand strategy and culture at Regent’s University London.
In an age of social media and with ever more divided public opinions, landing one universal message can be difficult, Wilson noted. For as long as that remains the case, some brands may still see value in taking a calculated risk.
“It’s hard to land one universal message, and even if you try and tailor your message to various groups, others are watching,” he said.
“Controversy grabs attention and puts you at the front of people’s minds. It splits crowds and forces people to have a decision when otherwise they probably wouldn’t care. That can lead to disproportionate publicity, which could be converted into sales.”
Carmakers claimed that leaving electric car sales rules unchanged would threaten British jobs and cost them hundreds of millions of pounds, according to documents that show the private lobbying for a slower transition away from fossil fuels.
BMW, Jaguar Land Rover, Nissan and Toyota claimed that rules forcing them to sell more electric cars each year would harm investment in the UK, according to responses to proposed changes submitted to the government. The responses were obtained by Fast Charge, a newsletter covering electric cars, and shared with the Guardian.
JLR, the Land Rover maker, said leaving the rules unchanged would “materially damage UK producers’ ability to invest in vehicle lines”.
The last Conservative government said last year that automotive manufacturers must sell an increasing proportion of electric cars each year, or else face steep fines, under rules known as the zero emission vehicle (ZEV) mandate.
Electric car sales have increased rapidly, accounting for more than a fifth of the market in July, and every carmaker complied with targets last year. However, carmakers earlier overestimated the demand for battery vehicles, meaning they have been forced to cut prices to attract buyers.
Lower prices are good for consumers, but the industry has argued they are unsustainable. After intensive lobbying, the Labour government backed down in April, adding new “flexibilities” to rules that will allow carmakers to sell more petrol cars.
The consultation responses reveal the detailed arguments that carmakers made in private in favour of leniency, despite advice from the government’s official climate adviser that the changes could raise UK carbon emissions.
The German manufacturer BMW said the UK had become worse for manufacturing since Brexit, but added that the ZEV mandate was “much more radical and far-reaching” than the equivalent rules in the EU or California.
BMW, which makes Mini and Rolls-Royce cars in Britain, wrote: “The UK has already become a far more difficult place to produce vehicles now post-Brexit, and a further challenging market environment could ultimately damage competitiveness and have a detrimental effect on the 8,000 jobs – up to 50,000 with supply chain – we currently retain in the UK.”
Japan’s Toyota, which runs factories in Derbyshire and north Wales, said “penalties could amount to hundreds of millions of pounds for individual manufacturers, a level that could place employment and investment across the industry at risk.” The world’s biggest carmaker by volume has focused on hybrid cars, combining a smaller battery and a petrol engine, and has lobbied successfully for hybrid sales to be allowed until 2035 in the UK.
Its Japanese rival Nissan, whose sole European factory is in Sunderland, said carmakers needed more flexibilities or else face “critical levels” of costs that would divert money “away from battery EV research and development in the UK”.
JLR, which has the most British factories, complained that a rule that allowed carmakers to buy “credits” from rivals whose electric car sales were above target meant that British companies were subsidising rivals particularly in China, which dominates electric car production.
However, campaigners counter that the rules worked by forcing carmakers to go electric.
Ben Nelmes, the chief executive of New Automotive, a group advocating the switch to electric vehicles, said: “The car industry’s own consultation responses confirm that the ZEV mandate’s 2024 targets were met, proving the policy is a powerful driver of change.
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“The focus should now shift to accelerating the transition, as this data shows the UK automotive industry is capable of delivering cheaper, cleaner transport.”
Tom Riley, the author of the Fast Charge newsletter, said: “Carmakers love to wave the union jack when it suits them, but threatening UK jobs and investment to weaken climate policy is a cynical tactic.”
Mike Hawes, the chief executive of the Society of Motor Manufacturers and Traders (SMMT), a lobby group, said: “The automotive industry faces unprecedented challenges, not least the shift to EVs against a subdued economic backdrop and fierce global competition. The ZEV mandate intensifies the pressure with the timescale necessitating brands spend billions to drive demand to achieve compliance. UK manufacturers have consistently warned that this cost was unsustainable and would threaten further investment.
He said the government was right to change previous targets, which would have meant “decarbonisation at the cost of de-industrialisation”.
A BMW spokesperson said the company supported UK and global climate targets, but added: “We believe consumers will ultimately determine the pace of transition to ZEVs, as mandates do not create demand.”
A Nissan spokesperson said: “We welcome the government’s pragmatic approach to lower-than-anticipated EV take-up, including the introduction of consumer incentives designed to bring consumer demand closer to ZEV mandate requirements.”
DOHA: Oil prices steadied on Friday amid uncertainty surrounding a potential peace deal between Russia and Ukraine, with prices gaining on the week for the first time in three weeks.
Brent crude futures settled up 6 cents or 0.09% to $67.73.
West Texas Intermediate (WTI) crude futures settled up 14 cents or 0.22% to $63.66.
Both contracts gained more than 1% in the previous session.
Brent gained 2.9% last week while WTI rose 1.4%.
US President Donald Trump said on Friday he will see if Russian President Vladimir Putin and Ukraine President Volodymyr Zelenskiy will work together in ending Russia’s war in Ukraine.
According to report by Al-Attiyah Foundation, the 3-1/2-year war continued unabated last week as Russia launched an air attack on Thursday near Ukraine’s border with the European Union, and Ukraine said it hit a Russian oil refinery and the Unecha oil pumping station, a critical part of Russia’s Europe-bound Druzhba oil pipeline.
Russian oil supplies to Hungary and Slovakia could be suspended for at least five days.
Meanwhile, US and European planners have presented military options to their national security advisers after the first in-person meeting between the US and Russian leaders since Russia invaded Ukraine.
Oil prices were also supported by a larger-than-expected drawdown from US crude stockpiles in the past week, indicating strong demand.
Stocks fell by 6 million barrels last week, the US Energy Information Administration said.
Asian spot liquefied natural gas (LNG) prices were slightly down last week on high storage inventories, continued weak demand and lack of progress on peace talks for Ukraine.
The average LNG price for October delivery into north-east Asia was at $11.40 per million British thermal units (mmBtu), down from $11.65 per mmBtu last week, industry sources estimated.
Analysts expect further downside to Asian LNG prices, as storage levels remain elevated, while the supply picture continues to firm.
Although Japan’s summer heat continues, demand for November heating is lagging.
Meanwhile, China is leaning more heavily on domestic gas and pipeline imports, reducing reliance on spot LNG and South Korea is well-stocked, exerting further downside pressure.
Moreover, some national oil companies (NOCs) in China were re-offering cargoes, while higher stocks limited injection demand, and strong hydro generation in Guangdong weighed on gas generation economics.
In Europe, gas prices steadied on Friday around firmer levels reached in the previous session, as attention turns to upcoming heavy maintenance in Norway and gas storage filling needs before the winter.
LNG imports into the continent remain healthy with expectations for an uptick in procurement of the super-chilled fuel ahead of the heating season.
The futures price at the Dutch TTF hub settled at $11.47 per mmBtu, recording a weekly gain of more than 8%.
Ether’s price has climbed 25% since the beginning of August, but historical data suggests the cryptocurrency could lose steam in September.
Only time will tell if Ether (ETH) plays out differently this year, with billions flowing into spot Ether ETFs and treasury companies.
Crypto trader CryptoGoos said in an X post on Friday, “ETH seasonality in September during post-halving years is typically negative. Will this time be different?”
Ether is trading at $4,759 at the time of publication, up roughly $952 from its Aug. 1 opening price of $3,807, according to CoinMarketCap. The crypto asset crossed new highs above $4,867 on Friday following dovish comments from US Federal Reserve Chair Jerome Powell at the Jackson Hole symposium.
History suggests caution for Ether during September
Powell hinted at a possible interest rate cut next month, which many in the crypto market view as a potential bullish catalyst.
However, history suggests caution for Ether as there have only been three instances since 2016 where Ether posted gains in August, and each was followed by a September decline, according to CoinGlass.
Since September 2016, Ether has delivered an average loss of 6.42%. Source: CoinGlass
In 2017, Ether surged 92.86% in August before dropping 21.65% the next month.
The pattern repeated in 2020, with a 25.32% gain in August followed by a 17.08% pullback in September. In 2021, Ether climbed 35.62% in August before slipping 12.55% in September.
Ether gained in the final three months of 2016 and 2020
Interestingly, even though September saw losses in 2016 and 2020, Ether posted upside in each of the following three months in both years.
However, this September could play out differently from previous years, with spot Ether ETFs and Ether treasury companies present, which were not active during past August rallies.
On Aug. 11, the total Ether held by companies with crypto treasuries surpassed $13 billion in value, as the cryptocurrency’s price surged past $4,300.
Source: Satoshi Stacker
On Saturday, blockchain intelligence firm Arkham reported that BitMine chairman Tom Lee bought another $45 million worth of Ether for the firm, bringing BitMine’s total stack up to $7 billion.
August has been a significant month for spot Ether ETFs
Meanwhile, spot Ether ETFs have seen roughly $2.79 billion net inflows in August alone, while spot Bitcoin (BTC) ETFs posted approximately $1.2 billion in net outflows over the same period, according to Farside.
Related: ETH data and return of investor risk appetite pave path to $5K Ether price
NovaDius Wealth Management president Nate Geraci said in a post on Saturday that there has been a “notable shift” in the inflows between spot Ether ETFs and spot Bitcoin ETFs.
Meanwhile, Bitcoin dominance, which measures its overall market share, has fallen 5.88% over the past 30 days to 58.19%, which many market participants typically attribute to capital rotating into the broader crypto market outside of BTC.
Magazine: ETH ‘god candle,’ $6K next? Coinbase tightens security: Hodler’s Digest, Aug. 17 – 23
Ros Rippon and Marie Caston said they spend a lot of time together outside of work including with each other’s families and called themselves “inseparable”
Two long-serving members of the critical care team at an NHS trust have been honoured for an incredible four decades of dedication to patient care.
Sisters Marie Caston and Ros Rippon, who both began their careers in 1985 at Peterborough City Hospital, are being celebrated by the North West Anglia NHS Foundation Trust for their combined 80 years of service.
The pair first met as young trainees, living opposite each other in the nurses’ home and became close while working together in the Intensive Care Unit — a friendship which has endured.
“They’d get our names mixed up all the time and only believed there were two of us once they saw us together,” they recalled.
NWAFT
A ceremony was held where the pair was thanked by the North West Anglia Foundation NHS Trust chairman Prof Steve Barnett OBE
Reflecting on their long careers, Ms Rippon said: “The sedation, equipment, and well, everything has changed.
“The care is much more specialised, and we now have 24-hour critical care consultants, whereas before it might have been a theatre consultant. More specialist care is available now, which is better.
“Every day is different, and there is always something to smile and laugh about.”
Ms Caston agreed with that sentiment: “Patients. Patients definitely. And all of the people we have met – we’re still in touch with the staff we’ve worked with over the years.”
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Marie Caston and Ros Rippon have been working at Peterborough City Hospital since 1985
The nurses shared admiration for the next generation of critical care nurses and reassured them that entering the profession was worth it.
“The new nurses are amazing – they know so much and do so much work. The pressure they are under is immense,” the pair agreed.
“It can often be really difficult, and you might not love it straight away, but if you stick it out it can be incredibly rewarding.”
Ms Rippon was also involved and initiated the critical care staff garden opened recently at the hospital, offering respite and peace.
A fox that was trapped in a fence and left hanging by its front legs has been rescued by four volunteers.
The male cub was discovered stuck between a fence and its concrete pillar and was set free with help from Fox Angles Foundation helpers, on Thursday in Leyton, east London.
He was taken to the South Essex Wildlife Hospital in Orsett, for X-rays, the charity said.
Vet Alda nursed him for a wound in his rump, cleaned up him and he was responding well to the treatment, it confirmed.
On Monday, another fox was freed and treated by the same animal hospital after he became trapped in a letterbox of a house in Hadleigh, near Southend.
The hospital said the “entrapment shows just how dangerous small gaps in fencing panels can be to wildlife”.
“With fox dispersal season now under way, these youngsters are still learning the world and need a little help to give them the best chance,” it said.
“If any of your fences have a narrow ‘V’-shaped gap, please do everything you can to fix it or barricade access.
This section presents a model for energy management of distributed generation sources in microgrids, which reduces energy supply costs. Therefore, the artificial bee colony optimization algorithm has been used for optimization. The goal of optimal use of resources in microgrids is to reduce operating costs. In addition to ABC algorithm, the Lightning Search Algorithm (LSA), Genetic Algorithm (GA), Particle Swarm Algorithm (PSO), and Modified Bat Algorithm (MBA) are used for optimization. It should be noted that all these algorithms were used in13. Studies have been repeated for four different radiation conditions: warm sunny, cold sunny, warm cloudy, and cold cloudy. In Figs. 5, 6 and 7, the results are shown as constant radiation in terms of global HZ irradiance (GHI), direct normal irradiation (DNI), and different seasons (Diff).
Fig. 5
Intensity of solar radiation in GHI conditions.
Fig. 6
Intensity of solar radiation in DNI conditions.
Fig. 7
Intensity of solar radiation in Diff conditions.
Next, in Fig. 8, the ambient temperature is shown in different conditions. Figure 9 illustrates the amount of electrical power requested in the microgrid for 24 h for four different situations.
Fig. 8
Ambient temperature in different conditions.
Fig. 9
The first scenario: (optimization results for a hot sunny day)
In the initial phase of the optimization, we focused on minimizing the operating costs of the microgrid under the conditions of a hot, sunny day. To achieve this objective, the optimization algorithms are employed to determine the most cost-effective power generation schedule for the microgrid during the specified period. It is important to note that each algorithm was executed 50 times to ensure statistical reliability, and the corresponding results are summarized in Table 2.
Table 2 Optimization results in the first scenario.
The results indicate that all the employed optimization algorithms produced relatively similar outcomes, with only slight differences in their objective function values. Nonetheless, even small variations in operational cost can accumulate to significant amounts over extended periods. For the ABC algorithm, the minimum and maximum values of the objective function were recorded at $363.21 and $391.17, respectively. In comparison, the GA yielded a minimum of $367.69 and a maximum of $380.34. A notable finding is that the standard deviation of the ABC algorithm was lower than that of the other optimization methods. A lower standard deviation suggests that the algorithm delivers more stable and accurate performance, reducing the likelihood of converging on an optimal solution by chance. Specifically, the standard deviation for the ABC algorithm was calculated to be 11.16, demonstrating greater consistency and reliability. In terms of reliability index, all algorithms achieved values below 2%. However, the ABC algorithm attained a reliability index of 1.45%, which is the lowest among the tested algorithms, further confirming its superior performance. Following optimization with the ABC algorithm, the power generation levels for each distributed energy resource were determined. If the output power of the distributed generation units is scheduled according to Figure (10), the total energy supply cost within the microgrid will be minimized under this scenario.
As anticipated, during peak electricity price hours, the electrical demand is primarily met by the distributed generation units. Conversely, during periods of low electricity prices, the utility grid supplies the required load power. Figures (11) illustrates the variation in the electricity price exchanged with the grid, as well as the battery’s charging and discharging profile, when optimized using the Artificial Bee Colony (ABC) algorithm.
The second scenario: (optimization results for a cold sunny day)
In the second scenario of the simulations, the cost reduction of using resources in the microgrid for a chilly summer day has been studied. As in the first scenario, optimization algorithms were used in new conditions to reduce costs. Algorithms have been executed 50 times; Table 3 shows the results.
Table 3 Optimization results in the second scenario.
Compared to the first scenario, a significant reduction in operational costs was observed, primarily due to decreased energy consumption. The best solution obtained using the proposed ABC algorithm was $292.86, while the optimal result from multiple executions of the GA reached $297.83. This indicates that the ABC algorithm achieved approximately $5 in cost savings over the GA method for microgrid optimization. In this scenario, a higher standard deviation was observed, suggesting greater dispersion in the optimization outcomes. However, the reliability index for the microgrid improved compared to the first scenario, indicating a reduction in the level of unsupplied energy. Specifically, the LPSP index was recorded at 1.23% for the ABC algorithm, consistent with the values obtained from other algorithms. After applying the ABC optimization algorithm, the generation power levels of each distributed energy source and the amount of electric power exchanged with the national grid were determined. Figure 10 illustrates the contribution of each distributed generation source in the second scenario.
Fig. 10
The contribution of each distributed generation source in the second scenario.
Similar to the first scenario, energy supply during peak electricity price hours was primarily managed using lower-cost distributed generation sources. During off-peak hours, the national grid provided a substantial portion of the microgrid’s load demand. The analysis of the second scenario also includes the hourly profile of electricity prices exchanged with the national grid and the battery storage’s charge/discharge behavior under optimization by the ABC algorithm. These results further confirm the algorithm’s ability to effectively manage energy resources while minimizing operational costs. Figure 11 shows the electricity price and battery power at different hours in the second scenario.
Fig. 11
Electricity price and battery power at different hours in the second scenario.
The third scenario: (optimization results for a hot cloudy day)
Continuing the process of simulations, in the third scenario, reducing the cost of using resources in the microgrid for a hot, cloudy day has been studied. Like the previous two scenarios, genetic algorithms, particle swarm, artificial bee, modified fake bee, lightning search, and the proposed artificial bee colony algorithm were used for optimization. The results of running the algorithm 50 times are given in Table 4.
Table 4 Optimization results in the third scenario.
The optimal, worst, and average values of the objective function obtained using the proposed ABC algorithm were $267.77, $300.38, and $289.61, respectively, with a standard deviation of $14.21. These results demonstrate superior performance compared to other optimization algorithms, indicating the consistency and reliability of the ABC algorithm in minimizing operational costs. In this scenario, the value of the LPSP reliability index increased, primarily due to reduced photovoltaic power generation caused by cloud cover and decreased solar irradiance, coupled with increased energy demand. When using the ABC algorithm, the LPSP index reached 1.76%. The hourly distribution of generation power for each distributed energy source and storage system, as well as the power exchanged with the national grid, is represented as a bar graph (Fig. 12). Notably, during high-cost electricity hours, the average exchanged power with the national grid was negative, indicating that electricity was sold to the grid, resulting in increased profitability for microgrid operators.
Fig. 12
The contribution of each distributed generation source in the third scenario.
Conversely, during low-cost electricity periods, such as early morning hours, a significant portion of the microgrid’s demand was met through purchases from the national grid. Additionally, batteries were primarily charged during these cheaper periods and discharged when electricity prices were higher, enhancing the overall economic performance of the system. Figure 13 shows the electricity price and battery power at different hours in the third section.
Fig. 13
Electricity price and battery power at different hours in the third scenario.
The fourth scenario: (optimization results for a cold, cloudy day)
Finally, in the fourth scenario of the simulations, the operation cost reduction in the studied microgrid for a cold, cloudy day has been done. Like the previous three scenarios, the optimization algorithms were executed 50 times, and their results are summarized in Table 5.
Table 5 Optimization results in the fourth scenario.
Following optimization using the ABC algorithm, the best result across 50 independent runs was recorded at $266.14, while the worst solution reached $340.83. For comparison, the best solutions obtained using other algorithms, GA, PSO, standard ABC, Improved Fake Bee Algorithm, and the LSA, were calculated as $271.51, $269.88, $268.17, $267.00, and $267.11, respectively. These findings highlight the superior performance of the proposed ABC method in minimizing microgrid operational costs. In this scenario, the reliability index, measured by the LPSP, remained below the acceptable threshold of 2% for all algorithms. However, the LPSP value resulting from the ABC algorithm was calculated at 1.61%, which is lower than all other methods tested, indicating better reliability and energy supply security. The hourly distribution of generation power from distributed energy resources, the battery’s charging and discharging behavior, and the energy exchanged with the national grid were also evaluated under ABC optimization. Figure 14 illustrates the contribution of each distributed generation source in the fourth section.
Fig. 14
The contribution of each distributed generation source in the fourth scenario.
As anticipated, during periods of high electricity prices, local distributed generation units were primarily responsible for supplying the load. Conversely, during hours with lower electricity prices, the majority of the microgrid’s energy demand was met through imports from the grid. Subsequently, the variation in electricity exchange prices and the battery’s charging/discharging profile under ABC optimization are illustrated, confirming an economically and operationally efficient energy management strategy. Figure 15 shows the electricity price and battery power at different hours in the fourth scenario.
Fig. 15
Electricity price and battery power at different hours in the fourth scenario.
Trade-off between operational cost and system reliability
The Artificial Bee Colony (ABC) algorithm effectively balances the trade-off between minimizing operational costs and ensuring system reliability in microgrid energy management. This is achieved through a penalty-based objective function, as defined in Eqs. (1)–(3), which minimizes costs related to fuel consumption, start-up, energy exchange with the national grid, and penalties for unserved load, while enforcing reliability constraints, specifically maintaining the Loss of Power Supply Probability (LPSP) below 2%. Candidate solutions that violate reliability constraints are penalized, guiding the algorithm toward solutions that optimize cost while ensuring reliable energy supply. The ABC algorithm’s adaptive mechanisms, including the waggle dance-inspired information sharing and scout bee exploration, enable efficient navigation of the solution space, avoiding local optima and ensuring consistent performance.
The performance of the ABC algorithm in managing this trade-off is evaluated across four operational scenarios (hot sunny, cold sunny, hot cloudy, and cold cloudy), with results compared against other metaheuristic methods: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Modified Bat Algorithm (MBA), and Lightning Search Algorithm (LSA). Table 6 summarizes the best operational cost and corresponding LPSP values for each algorithm across all scenarios, highlighting the ABC algorithm’s superior ability to achieve lower costs while maintaining high reliability.
Table 6 Comparison of operational cost and reliability across Scenarios.
As shown in Table 6, the ABC algorithm consistently achieves the lowest operational costs across all scenarios (e.g., $363.21 in Scenario 1, $292.86 in Scenario 2, $267.77 in Scenario 3, and $266.14 in Scenario 4) while maintaining the lowest LPSP values (1.45%, 1.23%, 1.76%, and 1.61%, respectively). This demonstrates its ability to optimize the cost-reliability trade-off effectively. For example, in Scenario 3 (hot cloudy day), where reduced photovoltaic generation due to cloud cover increases LPSP, the ABC algorithm still achieves the lowest cost ($267.77) and an LPSP of 1.76%, well within the acceptable limit of 2%. In contrast, GA and PSO yield higher costs ($273.18 and $271.56) and higher LPSP values (1.99% and 1.89%), indicating a less favorable trade-off.
The ABC algorithm’s superior performance can be attributed to its robust exploration and exploitation mechanisms. Unlike GA, which may converge to local optima due to its crossover and mutation operations, or PSO, which can be sensitive to parameter tuning, the ABC algorithm leverages a simpler structure with fewer control parameters. The scout bee phase ensures diversity in the solution space, while the onlooker bee phase refines promising solutions, leading to lower standard deviations (e.g., 16.11 in Scenario 1 vs. 19.85 for GA) and more consistent results. Compared to MBA and LSA, which perform well but achieve slightly higher costs and LPSP values, the ABC algorithm’s adaptive information-sharing mechanism (via the waggle dance) enhances its ability to balance cost and reliability effectively.
These results confirm that the ABC algorithm outperforms other metaheuristic methods in managing the trade-off between operational cost and system reliability, making it a robust and efficient tool for microgrid energy management under diverse operating conditions.
Performance of the ABC algorithm across different time horizons
The performance of the Artificial Bee Colony (ABC) algorithm in microgrid energy management varies depending on the scheduling time horizon—hourly, daily, or weekly—due to differences in computational requirements, data resolution, and the handling of uncertainties. The current study employs a daily (24-hour) scheduling horizon, optimizing the allocation of distributed energy resources (DERs) and battery storage on an hourly basis within a single day. This section evaluates how the ABC algorithm’s performance, in terms of computational time, operational cost, and system reliability (measured by Loss of Power Supply Probability, LPSP), is expected to vary across hourly, daily, and weekly scheduling horizons, based on the simulation results and typical algorithm behavior.
Hourly Scheduling: In an hourly scheduling framework, the ABC algorithm optimizes resource allocation for each hour independently or in short time blocks, using high-resolution forecasts of load, solar irradiance, and wind speed. This approach allows precise control of DERs and battery storage, enabling the algorithm to respond dynamically to short-term variations. However, frequent re-optimization increases computational demand, potentially leading to higher simulation times (e.g., estimated at 10–20 s per hour based on the daily simulation times of 84–125 s for 24 h). While operational costs and reliability remain comparable to daily scheduling (e.g., LPSP < 2%), the need for rapid computation may limit real-time applicability in large microgrids.
Daily Scheduling: The current study uses a daily scheduling horizon, as detailed in Sect. 6.1–6.4, where the ABC algorithm optimizes a 24-hour schedule based on hourly forecasts. This approach balances computational efficiency and solution quality, achieving operational costs of $266.14–$363.21 and LPSP values of 1.23–1.76% across the four scenarios (Tables 2, 3, 4 and 5). The daily horizon leverages a full day’s forecast, reducing the impact of short-term uncertainties while maintaining manageable computational times (84–125 s). This makes it well-suited for both planning and near-real-time applications in the tested benchmark microgrid.
Weekly Scheduling: For a weekly scheduling horizon (168 h), the ABC algorithm optimizes resource allocation over an extended period, requiring forecasts for load, solar, and wind profiles over seven days. While this enables strategic planning (e.g., for maintenance scheduling or energy trading), it significantly increases the solution space and computational complexity, potentially leading to simulation times in the range of 600–1000 s. Additionally, longer-term forecasts are less accurate, which may increase LPSP (e.g., estimated at 1.8–2.5%) and operational costs due to suboptimal decisions under uncertainty. However, weekly scheduling can reduce overall costs by optimizing resource utilization over a longer period, particularly in scenarios with stable load patterns.
Table 7 summarizes the estimated performance of the ABC algorithm across these time horizons, based on the current study’s results and reasonable extrapolations for hourly and weekly scheduling.
Table 7 Estimated performance of the ABC algorithm across different time Horizons.
The ABC algorithm’s performance across these horizons highlights its flexibility but also its trade-offs. Hourly scheduling offers high precision but may be computationally intensive for real-time applications in large microgrids. Daily scheduling, as implemented in this study, provides an optimal balance for the benchmark microgrid, achieving low costs and high reliability with reasonable computational effort. Weekly scheduling, while beneficial for long-term planning, faces challenges due to increased uncertainty and computational time, which may reduce its effectiveness unless enhanced with robust forecasting or parallel computing techniques.
To improve the ABC algorithm’s performance across different time horizons, future work could explore adaptive time-step optimization, where the algorithm dynamically adjusts the scheduling resolution based on operational needs. Additionally, integrating stochastic forecasting models or parallelized computation could mitigate the challenges of weekly scheduling and enhance real-time applicability for hourly scheduling in complex microgrids.
Discussion
The simulation results presented in Sect. 6.1–6.6 highlight the effectiveness of the Artificial Bee Colony (ABC) algorithm in optimizing energy management for a benchmark microgrid under diverse load and solar irradiance conditions. Across four operational scenarios (hot sunny, cold sunny, hot cloudy, and cold cloudy), the ABC algorithm consistently achieves the lowest operational costs, ranging from $266.14 to $363.21, while maintaining high system reliability, with Loss of Power Supply Probability (LPSP) values between 1.23% and 1.76%, well below the 2% threshold (Tables 2, 3, 4 and 5). These findings, particularly those in Subsection 6.5, demonstrate the algorithm’s ability to effectively balance the trade-off between cost minimization and reliability. The ABC algorithm outperforms other metaheuristic methods, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Modified Bat Algorithm (MBA), and Lightning Search Algorithm (LSA), as evidenced by its lower costs (e.g., $266.14 vs. $271.51 for GA in Scenario 4) and lower LPSP values (e.g., 1.61% vs. 1.88% for GA in Scenario 4). The algorithm’s lower standard deviation (e.g., 16.11 in Scenario 1 compared to 19.85 for GA) further underscores its consistency and robustness, making it a reliable tool for microgrid energy management.
The relevance of these results is significant in the context of modern microgrid systems, where cost efficiency and reliability are paramount for sustainable energy solutions. As discussed in Subsection 6.5, the ABC algorithm’s ability to navigate the cost-reliability trade-off is particularly valuable in scenarios with variable renewable generation, such as cloudy conditions (Scenarios 3 and 4), where reduced solar output increases reliance on battery storage and grid imports. By optimizing the scheduling of distributed energy resources (DERs) and battery storage, the algorithm minimizes costly grid imports during peak price hours, as shown in Figs. 16, 17, 10, 11, 12, 13, 14 and 15. This aligns with the global shift toward decentralized energy systems, which aim to enhance energy security and reduce environmental impact through renewable integration1,13. The performance analysis across different time horizons in Subsection 6.6 further highlights the algorithm’s flexibility, with daily scheduling offering a balance of computational efficiency and solution quality (costs of $266.14–$363.21, LPSP of 1.23–1.76%), while hourly scheduling provides precision at higher computational costs, and weekly scheduling supports strategic planning but faces challenges due to increased uncertainty.
Fig. 16
The contribution of each distributed generation source in the first scenario.
Fig. 17
Electricity price and battery power at different hours in the first scenario.
Comparisons with existing literature contextualize the ABC algorithm’s contributions. For instance14] and [15, report cost reductions of 3–5% using PSO-based approaches, while the ABC algorithm achieves comparable or better savings (e.g., 5% lower costs than GA in Scenario 2). The integration of battery storage optimization, as explored in23, complements the current study’s focus on efficient resource utilization. However, Subsection 6.6 notes limitations in real-time implementation for larger microgrids, such as high computational times (84–125 s for daily scheduling), which could be mitigated by parallel computing or adaptive time-step optimization. These findings align with46, which emphasizes the role of adaptive controllers in enhancing microgrid performance, suggesting that combining ABC with such controllers could further improve real-time applicability.
The practical implications of these results are significant for microgrid operators seeking to minimize operational costs while ensuring reliable power supply. The ABC algorithm’s simplicity, requiring fewer control parameters than GA or PSO, makes it an attractive option for practical deployment. However, challenges such as computational complexity in large-scale systems and sensitivity to forecasting errors, as discussed in related literature48, must be addressed to fully realize its potential. Future research could explore integrating stochastic optimization or advanced forecasting techniques to enhance robustness, as well as incorporating demand response strategies45 to further reduce costs by 2–5%. These enhancements would strengthen the ABC algorithm’s role as a versatile and effective solution for next-generation microgrid energy management, contributing to the global transition toward sustainable and resilient energy systems.
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