Energy Cost Savings Research Articles

Effects of Hot-Spring Water Culture on the Nutritional Value of Muscle from Japanese Eel Anguilla japonica

ABSTRACT The chemical composition of muscles from Japanese eels cultured in hot-spring water was determined and compared with that of eels cultured in well water. The proximate composition did not differ significantly between the culture conditions, but the hepatosomatic indices of the hot-spring water eels were significantly higher than those of the well water eels. Amino acid levels were higher in the hot-spring water eels, and the carnosine content of the hot-spring eels was lower than that of the controls. Our results suggest that hot-spring water is a promising medium for eel culture, and its use may bring energy cost savings.

Model predictive control for smart buildings: a simulation approach within Indian context

PurposeIn the context of a developing country, Indian buildings need further research to channelize energy needs optimally to reduce energy wastage, thereby reducing carbon emissions. Also, reduction in smart devices’ costs with sequential advancements in Information and Communication Technology have resulted in an environment where model predictive control (MPC) strategies can be easily implemented. This study aims to propose certain preemptive measures to minimize the energy costs, while ensuring the thermal comfort for occupants, resulting in better greener solutions for building structures.Design/methodology/approachA simulation-based multi-input multi-output MPC strategy has been proposed. A dual objective function involving optimized energy consumption with acceptable thermal comfort has been achieved through simultaneous control of indoor temperature, humidity and illumination using various control variables. A regression-based lighting model and seasonal auto-regressive moving average with exogenous inputs (SARMAX) based temperature and humidity models have been chosen as predictor models along with four different control levels incorporated.FindingsThe mathematical approach in this study maintains an optimum tradeoff between energy cost savings and satisfactory occupants’ comfort levels. The proposed control mechanism establishes the relationships of output variables with respect to control and disturbance variables. The SARMAX and regression-based predictor models are found to be the best fit models in terms of accuracy, stability and superior performance. By adopting the proposed methodology, significant energy savings can be accomplished during certain hours of the day.Research limitations/implicationsThis study has been done on a specific corporate entity and future analysis can be done on other corporate or residential buildings and in other geographical settings within India. Inclusion of sensitivity analysis and non-linear predictor models is another area of future scope.Originality/valueThis study presents a dynamic MPC strategy, using five disturbance variables which further improves the overall performance and accuracy. In contrast to previous studies on MPC, SARMAX model has been used in this study, which is a novel contribution to the theoretical literature. Four levels of control zones: pre-cooling, strict, mild and loose zones have been used in the calculations to keep the Predictive Mean Vote index within acceptable threshold limits.

Energy-saving, economic and environmental benefits analysis of cool roofs and green roofs for retail buildings in America

Green roofs and cool roofs are considered to improve energy efficiency, and provide economic benefits, energy-saving benefits, environmental benefits for the traditional grey roofs. Some national and local standards have analysed and recommended these technologies in existing building renovations, but their research subjects were mostly office or residential buildings, which didnt include the evaluation and analysis of specific roof renovations in different thermal zones in the United States. This paper conducted a 40-year life cycle cost analysis of retail buildings roofs in 16 cities across 8 different thermal zones in the United States. LCCA found that cool roofs showed a positive life cycle NPV in all cities, while green roofs showed a negative NPV. This article also analysed and compared the environmental benefits of retail buildings roofs in 16 cities, and calculated the emission reductions that they can bring to power plants. This paper also selected retail buildings roofs from 8 representative cities in 8 different thermal zones for energy savings and cost savings analysis. The results show that decision-makers can transform ordinary roofs into green roofs or cool roofs according to the characteristics of each thermal zone, combined with energy-saving benefits, economic benefits, environmental benefits and other factors, which is conducive to the implementation of new specifications.

A multi-objective optimization framework for building performance under climate change

Most building energy simulations utilize typical meteorological year data, overlooking the impacts of climate change. This can lead to inaccurate predictions and suboptimal designs. This study develops an optimization framework to identify robust building designs under varying climate scenarios. A case study is presented for residential buildings in Huangshan, China under 2020, 2050 and 2020–2050 conditions. A general circulation model provides future weather data. Multi-objective optimization maximizes energy efficiency and minimizes life cycle costs. Machine learning models predict heating, cooling and cost as functions of envelope parameters. Optimization is performed using a genetic algorithm. Results reveal the optimum insulation thickness, wall properties and window specifications. Compared to 2020 data, the ideal roof insulation increases from 40 mm to 90 mm by 2050. The optimal heating transmittance decreases from 0.38 W/m2K to 0.19 W/m2K. The approach highlights the significant influence of evolving climate on building design. It provides architects with accurate projections to make informed design decisions for energy efficiency and cost savings over the building lifespan.

Distributed clean energy opportunities for US oil refinery operations

The oil and gas industry is increasingly seeking operational improvements to reduce costs and emissions while improving resilience. This study describes techno-economic analysis of opportunities for distributed energy resources that could be integrated to support oil and gas companies’ economic, environmental, and energy resiliency goals. Specifically, the analysis evaluates solar photovoltaics, wind turbines, battery energy storage, landfill gas, biomass, municipal solid waste-to-energy, solar steam for process heat, combined heat and power, and electrolyzers for hydrogen production at two hypothetical refineries, one located in Louisiana and the other in southern California. These technologies could reduce the sites’ consumption of grid electricity and/or natural gas and thus can help reduce emissions. This study employs the ReOPT tool and System Advisor Model to evaluate the techno-economic potential for clean energy technologies to support refineries in achieving energy goals, including energy cost savings, resiliency, and emissions reductions. Results indicate that the associated costs of emissions reductions via several distributed clean energy technologies are competitive with other emissions reduction strategies such as energy efficiency, reducing flaring, direct carbon capture and sequestration, and markets under certain conditions. There are also cost beneficial opportunities for the use of renewable energy for refining, especially for resilience, depending on local conditions such as resources and utility costs.

Comparative Analysis of Residential Energy Consumption in Selected Areas of Cantilan Surigao del Sur

Energy consumption is considered as one of the most crucial issues in our daily lives, yet, the fundamental understanding of how energy assessment and policies are designed remained as one of the biggest barriers because of the complexity. The study is focused on analyzing the residential energy consumption in selected barangays in the municipality of Cantilan, Surigao del Sur, Mindanao Philippines. The study provides well-documented data as basis for establishing policy for potential energy usage reductions and cost savings of the municipality. The study investigates the profile of the respondents, profile of household based on construction type; and residents’ contributory factors and practices on energy consumption. The descriptive research design was employed through survey and informal interviews from the two most populated barangays in the municipality of Cantilan. A validated researcher-made questionnaire served as the main tool in the data gathering. Based on the findings, the contributory factors on energy consumption and the conservation practices of the respondent barangays mirrored their socio-economic status. Most respondents fall into low-income households, where only those essential appliances useful in their day-to-day activities are the priorities and things they afforded. Although most households only have a few appliances, the average monthly electric expense has increased due to high energy costs and frequent energy interruptions. The respondents’ conservational habits are reflected in their energy consumption. It can be deduced that understanding the consumers’ motivations of energy usage in a specific situation, will eventually lead to gaining insights of good energy consumption process in the area.

Benefits through Space Heating and Thermal Storage with Demand Response Control for a District-Heated Office Building

Demand response techniques can be effective at reducing heating costs for building owners. However, few studies have considered the dynamic marginal costs for district heating production and taken advantage of them for building-level demand response. In this study, a district heating network in the Finnish city of Espoo was modeled to define dynamic district heat prices. The benefits of two demand response control approaches for a Finnish office building, the demand response control of space heating and a thermal energy storage tank, were evaluated by comparing them to each other and utilizing them together. A 5 m3 storage tank was installed in a substation of a conventional high-temperature district heating network. A new demand response control strategy was designed to make the most of the storage tank capacity, considering dynamic district heat prices and the maximum allowed return water temperature. The results indicate that the demand response control of space heating and the storage tank cut district heat energy costs by 9.6% and 3.4%, respectively. When employing the two approaches simultaneously, 12.8% savings of district heat energy costs were attained. Additionally, thermal energy storage provides more potential for peak power limiting. The maximum heating power decreases by 43% and the power fee reduces by 41.2%. Therefore, the total cost, including the district heat energy cost and the power fee, can be cut up to 22.4% without compromising thermal comfort and heat supply temperatures to ventilation systems.

Maximising the benefit of variable speed heat-pump water heater with rooftop PV and intelligent battery charging

Domestic applications of thermal energy storage, interacting with rooftop PV and batteries can substantially reduce consumer electricity costs, whilst assisting grid stability. Air source heat pump water heaters (HPs) with inherent thermal storage offer significant purchased energy savings, however, their typical control only considers stored water temperature. We demonstrate that further energy and cost reductions are possible using variable compressor speed controls that optimise thermal performance in real-time, whilst coordinating energy consumption to match available PV or shifting consumption to off-peak hours. The incremental complexity of variable-speed HP water heaters can reduce HP electricity consumption from the grid by up to 28% without PV or battery, 88% with PV, and 99% using PV and battery storage. By integrating the HP and all other domestic electrical loads, we exploit the combination of thermal and battery energy storage using rules-based and machine-learnt hierarchical controls to reduce the net household energy cost. Machine-learnt grid battery charging is shown to reduce annual grid energy consumption by 84%, delivering 99% of the potential energy cost savings. We conclude that a HP with inherent thermal storage, interacting with rooftop PV offers the most cost-effective home energy storage solution, lowering net lifetime costs by up to 27%.

Physically consistent deep learning-based day-ahead energy dispatching and thermal comfort control for grid-interactive communities

Grid-interactive communities are an emerging solution for optimal energy dispatching, improving grid stability, and enhancing the usage of on-site renewable energy by leveraging community flexibilities. This work introduces a novel Physically Consistent Deep Learning (PCDL)-based optimization framework for day-ahead energy dispatching and thermal comfort control within grid-interactive communities. Specifically, the PCDL model is developed to capture the thermal dynamics within the community while ensuring strict adherence to physics consistency. This consistency is defined with the aim of generating physically viable solutions in response to modified system inputs. Subsequently, the PCDL model is utilized for load estimation and indoor climate prediction within a proposed scheduling and control strategy: (1) an optimal energy dispatching plan is calculated based on the day-ahead grid market; (2) a real-time model predictive control (MPC) method is applied to minimize the utilization error and indoor comfort constraint violations caused by following the scheduled energy dispatching plan. A simulation case of a residential hall community at Cornell University with on-site renewable energy resources is implemented to demonstrate the effectiveness of the proposed approach. Comparative simulations, which include the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Gaussian Process (GP) regression models, confirm the performance advantage of capturing community thermal dynamic and control-oriented generalization ability when using the PCDL model. These results leads to notable improvements in indoor thermal comfort control, ranging between 65.4 and 68.7%, 63.6–79.3%, and 60.4–62.2% for each comparative model, respectively. Moreover, by harnessing community demand flexibility, we achieve energy cost savings between 29.5 and 39.7% compared to the baseline controller.

A Guide to the Integration and Utilization of Energy Storage Systems with a Focus on Demand Resource Management and Power Quality Enhancement

The increasing peak electricity demand and the growth of renewable energy sources with high variability underscore the need for effective electrical energy storage (EES). While conventional systems like hydropower storage remain crucial, innovative technologies such as lithium batteries are gaining traction due to falling costs. This paper examines the diverse applications of energy storage, spanning from grid connectivity to end-user solutions, and emphasizes large-scale energy recovery and system stability. The integration of EES with various energy infrastructures and consumer strategies is explored, highlighting the use of tariffs and peak pricing systems for energy cost savings. Country-specific priorities shape EES deployment, with the U.S focusing on grid stability, Japan on emergency power, and South Korea, still in the demonstration phase, prioritizing peak demand reduction. Our analysis of the UK, U.S., and South Korea reveals the pivotal role of energy storage in achieving flexible and efficient energy systems. The industry shows promising growth, with significant commercial expansion expected around 2035, presenting profound policy and deployment implications for the future.

Demand side flexibility for a Heat Booster Substation with ultra low temperature district heating

Ultra-low temperature district heating (ULTDH) systems improve the heat distribution efficiency by reducing thermal network losses and provide an easier access to Renewable Energy Sources (RES) by utilizing them as heat sources through Power-to-Heat solutions. In this study, Domestic Hot Water (DHW) preparation at a Heat Booster Substation (HBS) supplied with ULTDH is investigated for generating demand side flexibility. The main components of the HBS are a Hot Water Storage Tank (HWST) and an electric-driven heat pump. A genetic algorithm based control strategy is developed to optimally utilize the thermal energy storage capability of the HBS by intelligently scheduling the charging of the HWST based on time-varying electricity price signals. A multi-objective control strategy is designed to reduce energy costs while generating flexibility on the demand side. The components of the HBS are numerically modeled and experimentally validated to form a dynamic non-linear model of the HBS. Further, the data-driven seasonal ARIMA model, ARIMA(0,1,1)x(0,1,1)168, is developed to forecast the DHW consumption based on a statistical analysis of the historic consumption profiles over a two-year period. The forecast RMSE of the DHW prediction model is less than 0.03kgs−1 over the two-year test period, indicating that the prediction model is able to accurately forecast the DHW consumption of the residents. With the control-oriented HBS numerical model and the DHW prediction model serving as decision support tools, the developed control strategy is implemented at the HBS. Results show that in comparison to a rule-based control strategy, the developed control strategy demonstrates significant demand side flexibility while attaining energy cost savings of more than 91% and 84% at time resolutions of 15 and 30 min for the control interval respectively. With increased volatility in electrical prices due to higher penetration of RES, greater energy cost savings of more than 150% are observed.

IoT-assisted energy cooperation in adjacent buildings with interrupted utility

Facing severe power outages primarily due to generation shortfalls and transmission line losses, Pakistan is seeing an overwhelming shift in renewable energy sources both in residential and industrial buildings. Motivated by the same, efficient load sensing and management techniques – already available in the literature – are being adopted in modern construction their efficiency in terms of cost-savings, however, is still questionable. In this work, an Internet-of-Things (IoT) assisted energy cooperation scheme for adjacent buildings equipped with renewable energy sources is proposed for enhanced efficiency in energy management, with additional generators to cater for the power outages. The IoT network consists of a combination of local and global aggregators; while each building is equipped with a local aggregator to determine the current state of electricity, heating and cooling consumption and generation, the global aggregator, on the other hand, acts as an intelligent hub for energy cooperation. The latter determines the amount of energy to be shared among the buildings to minimize the overall energy cost. For theoretical realization of the proposed scheme, a comprehensive energy cooperative mathematical model is developed, whose objective function is presented as a nonlinear optimization problem – linearized using McCormick’s Envelopes. Our simulation results reveal that the proposed framework promises increased efficiency in terms of energy cost-savings; we support our claims with a detailed quantitative analysis.

Deep reinforcement learning for fuel cost optimization in district heating

This study delves into the application of deep reinforcement learning (DRL) frameworks for optimizing setpoints in district heating systems, which experience hourly fluctuations in air temperature, customer demand, and fuel prices. The potential for energy conservation and cost reduction through setpoint optimization, involving adjustments to supply temperature and thermal energy storage utilization, is significant. However, the inherent nonlinear complexities of the system render conventional manual methods ineffective. To address these challenges, we introduce a novel learning framework with an expert knowledge module tailored for DRL techniques. The framework leverages system status information to facilitate learning. The training is performed by employing model-free DRL methods and a refined digital twin of the Espoo district heating system. The expert module, accounting for power plant capacities, ensures actionable directives aligned with operational feasibility. Empirical validation through comprehensive simulations demonstrates the efficacy of the proposed approach. Comparative analyses against manual methods and evolutionary techniques highlight the approach's superior ability to curtail fuel costs. This study advances the understanding of DRL in district heating optimization, offering a promising avenue for enhanced energy efficiency and cost savings.

Inefficient markets for energy efficiency? – The efficiency premium puzzle in the German rental housing market

On the market where prospective renters meet dwelling offers, competitive forces and rational behavior on both sides would imply that the monthly basic rent should reflect differences in expected monthly heating costs – other things being equal. We test this hypothesis by specifying a hedonic price model reflecting a total-cost-of-renting perspective. Drawing on 844,229 apartment listings for rent from 2014 to 2020 on a small spatial scale, we find a premium for more energy-efficient apartments; however, it is rather small. If the energy performance score decreases by 10 kWh/m2a, the monthly basic rent increases, on average, by roughly €0.01 per square meter living area. The expected energy cost savings thereby exceed the premium by a factor of three to seven. Rather, we find discounts of up to 9.2 % if apartments use heating technologies that are known to be inefficient. We explore various explanations for these outcomes, considering both landlord and renter behavior.

ENERGY BALANCE ANALYSIS; CASE STUDY OF RURAL ELECTRIFICATION AGENCY (REA) PROJECT IN UNIVERSITY OF MAIDUGURI

University of Maiduguri has been faced with power supply challenges due to insurgency that negatively affects the north eastern part of Nigeria. Being a research institution, increases in the costs of power and instability of on-grid power supply have jetlagged almost comatose research and academic activates. Government through the Rural Electrification Agency (REA) has provided the University with solar powered streetlights to curb energy consumption and to improve security. This study performed comparative energy cost analysis between solar photovoltaics streetlights executed by REA and public powered streetlights to evaluate energy costs. The objective of this study is to analyse the cost benefits of using solar photovoltaics streetlights, against public powered streetlight. The analyses showed 58% savings in energy costs as against 7.99% rise for public powered streetlights in 2019. The usage of solar photovoltaic streetlights has saved the University of Maiduguri close to 14.8 million Naira from year 2017 to 2019 using 134 solar streetlights poles with 77.22% fil factor.

A deep learning approach to predict and optimise energy in fish processing industries

The fish processing sector is experiencing increased pressure to reduce its energy consumption and carbon footprint as a response to (a) an increasingly stringent energy regulatory landscape, (b) rising fuel prices, and (c) the incentives to improve social and environmental performance. In this paper, a standalone forecasting computational platform is developed to optimise energy usage and reduce energy costs. This short-term forecasting model is achieved using an artificial neural network (ANN) to predict power and temperature at thirty-minute intervals in two cold rooms of a fish processing plant. The proposed ANN function is optimised by genetic algorithms (GA) with simulated annealing algorithms (SA) to model the relationships between future temperature and power and the system variables affecting them. To assess the accuracy of the proposed method, extensive experiments were conducted using real-world data sets. The results of the experiments indicate that the proposed ANN model performs with higher accuracy than (a) the long short-term memory (LSTM) as an artificial recurrent neural network (RNN) architecture, (b) peephole-LSTM, and (c) the gated recurrent unit (GRU). This paper finds that using GA & SA algorithms; ANN parameters can be optimised. The RMSE obtained by the ANN compared with the second-ranked method GRU was consequently 16% and 4% less for the predicted temperature and power. The results in one particular site demonstrate energy cost savings in the range of 15%–18% after applying the forecast-optimiser approach. The proposed prediction model is used in a fish processing plant for energy management and is scalable to other sites.

SolarEV City Concept for Paris

Urban decarbonization is one of the pillars for strategies to achieve carbon neutrality around the world. However, the current speed of urban decarbonization is insufficient to keep pace with efforts to achieve this goal. Rooftop photovoltaics (PVs) integrated with electric vehicles (EVs) as battery is a promising technology scalable to supply CO2-free, affordable, and dispatchable electricity in urban environments (“SolarEV City Concept”). Here, we evaluated Paris, France for the decarbonization potentials of rooftop “PV + EV” in comparison to the surrounding suburban area Ile-de-France and the reference city Kyoto, Japan. We assessed various scenarios by calculating energy sufficiency, self-consumption, self-sufficiency, cost savings, and CO2 emission reduction of the PV + EV system or PV only system. We found that above a certain roof coverage 50–60% of the total roof area for Paris or 20–30% for Ile-de-France, PV electricity production regularly exceeds the demand. Above that roof coverage, feed-in-tariffs (FIT) or storage are needed to further realize the potential of the rooftop PV system. The combination of PVs with EVs is more effective in Ile-de-France than in Paris because large surplus electricity from rooftop PV in suburban area requires large storage. With already low-carbon electricity of France by nuclear power, the maximum potential CO2 abatement through the implementation of SolarEV City Concept in Paris (0.020 kgCO2/kWh reduction from 0.063 kgCO2/kWh) is limited in comparison to that in Kyoto (0.270 kgCO2/kWh reduction from 0.352 kgCO2/kWh). However, with declining costs of PVs and EVs in the coming decades, low-cost dispatchable electricity by the PV + EV system throughout Paris and Ile-de-France may facilitate the electrification of CO2 emitting appliances, potentially allowing rapid CO2 emission reductions from Paris.

A toolchain for domestic heat-pump control using Uppaal Stratego

Heatpump-based floor-heating systems for domestic heating offer flexibility in energy consumption patterns, which can be utilized for reducing heating costs—in particular when considering hour-based electricity prices. Such flexibility is hard to exploit via classical Model Predictive Control (MPC), and in addition, MPC requires a priori calibration (i.e., model identification) which is often costly and becomes outdated as the dynamics and use of a building change. We solve these shortcomings by combining recent advancements in stochastic model identification and automatic (near-)optimal controller synthesis. Our method suggests an adaptive model-identification using the tool CTSM-R, and an efficient control synthesis based on Q-learning for Euclidean Markov Decision Processes via Uppaal Stratego. This paper investigates three potential control strategy perspectives (i.e., fixed-target, target-band, and setbacks) to achieve energy efficiency in the heating system. To examine the performance of the suggested approaches, we demonstrate our method on an experimental Danish family-house from the OpSys project. The results show that a fixed-target strategy offers up to a 39 % reduction in heating cost while retaining comparable comfort to a standard bang-bang controller. Even better, target-band and setbacks strategies gain up to 46-49 % energy cost savings. Furthermore, we show the flexibility of our method by computing the Pareto-frontier that visualizes the cost/comfort tradeoff. Additionally, we discuss the applicability of Stratego for an old-fashioned binary-mode heat-pump system and report significant cost savings (33 %) as compared to the bang-bang controller. Moreover, we also present the performance analysis of Stratego against an industry-standard control strategy.

Sustainable solutions for healthcare facilities: examining the viability of solar energy systems

The healthcare sector is responsible for a significant portion of global carbon dioxide emissions, accounting for approximately 5% of the total. As energy demand in the sector continues to rise, sustainable solutions are urgently needed. Hospitals and healthcare facilities require a range of engineering services, including heat ventilation and air conditioning systems, hot and domestic water supply systems, and backup electricity systems. These energy-intensive services offer an excellent opportunity to integrate renewable energy sources and reduce the carbon footprint of healthcare facilities. This study presents a case study of a hospital located in the Gulf Cooperation Council (GCC) that utilizes a solar-collected water-heated system. The research aims to investigate the impact of adding multi-solar collector and photovoltaic systems to healthcare facilities, analyze the system’s thermodynamic efficiency in terms of energy and exergy, assess its technical and economic viability, and gauge the adoption rate of solar systems by healthcare technical departments. The results demonstrate that the solar thermal system provides around 12% of the total energy needed for the hot water system, while the solar PV system contributes approximately 29.6% of the total load for the HVAC system. This study explores the potential of using solar energy systems in healthcare facilities in the GCC region, analyzing their technical, thermodynamic, and economic viability. It promotes the adoption of solar systems in GCC and Middle East healthcare facilities and identifies research gaps related to solar systems implementation in healthcare facilities in the GCC. The study highlights the potential benefits of solar energy systems in terms of energy efficiency, cost savings, and environmental sustainability, with implications for healthcare facilities in the region and beyond. By utilizing renewable energy in healthcare facilities, the sector can reduce its carbon footprint and contribute to a more sustainable future.

Analysis of Energy Recovery from Municipal Solid Waste and Its Environmental and Economic Impact in Tulkarm, Palestine

Municipal solid waste management (SWM) is one of the most useful applications of renewable energy, supporting the market for renewable energy and assisting in environmental protection by reducing emissions. Sustainability is one of the key issues with solid waste management systems in developing countries, including Palestine. Waste-to-energy (WTE) technology is one of the greatest strategies for effectively managing solid waste. This study focuses mostly on waste management in Palestine’s Tulkarm area. We conducted an evaluation of the potential of energy recovery from municipal solid waste through a number of approaches, such as incineration, gasification, anaerobic digestion, and landfilling. Additionally, the implications on the environment, human health, and economics were investigated. The results showed that anaerobic digestion recovered around 5156.15 kWh per day; whereas, incineration and gasification recovered about 40,986.60 and 14,663.88 kWh per day, respectively. The least recovered energy amount was roughly 3563.87 kWh per day, which was generated by landfilling technology. Additionally, the environmental analysis showed that anaerobic digestion could save around 48,362 tons of CO2 annually; meanwhile, incineration and gasification could save about 384,424 and 137,538 tons of CO2, respectively. The lowest quantity of emissions could be saved by landfilling, with about 33,427 tons saved yearly. The energy cost savings associated with each technology were also estimated. The results of the energy savings estimation showed that incineration had the highest saving of USD 4918.12; gasification and anaerobic digestion saved USD 1759.67 and USD 618.74, respectively. Whereas, landfilling had the lowest saving of about USD 427.66. A careful analysis of all aspects related to each technology, in comparison to the available waste treatment techniques, was carried out to propose a solution to cover the energy deficit in Tulkarm as a primary goal. Anaerobic digestion was found to be more environmentally advantageous and economically feasible and thus can be recommended to decision-makers and investors.