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Technical, economic, and environmental assessment of a stand-alone power system based on diesel engine with/without energy storage using an optimization algorithm: A case study in China

  • Nexus between Energy Consumption, Economic Development, and Environmental Impacts: Find Solutions to Reduce Environmental Degradation by Production Technologies and Systems
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Abstract

In stand-alone power systems, technical, economic, and environmental (TEE) assessment of hybrid energy systems under uncertainty is an important issue. This paper focuses on the TEE assessment of a stand-alone hybrid energy system composed of photovoltaic (PV) and diesel generator (DG) with/without battery energy storage (BS) in remote islands in China. So, determining the optimal sizes of PV and DG with/without BS for economic, reliable, and efficient operation of a hybrid power system in a microgrid is important. For this goal, a modified swarm intelligence algorithm is used to optimize, techno-economic feasibility and avoid potential CO2 emission. To demonstrate the effectiveness of the modified swarm intelligence algorithm, it is compared with the standard swarm intelligence method and simple simulated annealing method in terms of operational cost reduction and power loss reduction. The aim of the optimization is to minimize the cost of a stand-alone solar power system based on diesel engine with/without battery energy storage system by optimal determination of the load uncertainty and CO2 emission. The optimal results are developed further by performing sensitivity analysis, such as the effect of the fuel cost and the penalty cost of CO2 emission. Over the case study, simulation results show that the proposed algorithm obtains more promising results in terms of TEE aspects. The reliability, low carbon, and cost-effectiveness of stand-alone solar power systems based on diesel engine with battery energy storage system can be easily calculated using the correlations derived in this analysis. The resulting cost of energy is in the range of 0.2845 to 0.6492 $/kWh.

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Abbreviations

Ca BS :

Battery rated capacity (kWh)

C inv , i :

Investment cost of the i-th component ($)

C O & M , i :

O&M cost of the i-th component ($)

CF :

Constriction factor

c1 and c2 :

Trust parameters

C re p ,i,j :

Replacement cost of the i-th component during the j-th year ($)

g best :

Global best solution

ir:

Interest rate (%)

LT:

Project lifetime (year)

P LD :

Load demand (kW)

P dump :

Dump load (kW)

P BS ,in :

Available input power of battery (kW)

P PV :

PV power production (kW)

P PV-rated :

Rated power of the PV panel (W)

P DG :

DG power production (kW)

P BS ,out :

Available output power of the battery (kW)

P BS ,ch :

Charging power of the BS (kW)

P BS ,dc :

Discharging power of the BS (kW)

p best :

Personal best solution

P deficit :

Electrical load not covered by the hybrid energy system (kW)

ph i :

Coefficient of contraction

BS:

Battery energy storage

COE:

Cost of energy

CPSO:

Constriction coefficient PSO

CRF:

Capital recovery factor

DG:

Diesel generator

EMS:

Energy management strategy

IN:

Inverter

LPSP:

Loss of power supply probability

r 1 and r 2 :

Random numbers

SOCBS :

Battery state of charge (kWh)

\(SO{C}_{BS}^{{\text{max}}}\) :

Maximum value of SOC (kWh)

\(SO{C}_{BS}^{{\text{min}}}\) :

Minimum value of SOC (kWh)

S r ,ref :

Reference radiation (W/m2)

S r :

Solar radiation (W/m2)

SD:

Hourly self-discharge rate (%)

S i :

i-Th component of the hybrid energy system (with i = PV, DG, BS, and IN)

S i, min :

Lower limit of the size of the i-th component

S i, max :

Upper limit of the size of the i-th component

T ref :

Reference temperature (oC)

T c :

Temperature of the cell (oC)

T a :

Ambient temperature (oC)

T NOCT :

Cell temperature at nominal conditions (oC)

\({v}_{i}^{k}\) :

Particle velocity in iteration k

w :

Inertia weight

\({x}_{i}^{k}\) :

Particle position in iteration k,

α T :

Temperature coefficient (1/oC)

Δt :

Time resolution (h)

η B S ,ℎ :

Charging efficiency of the BS (%)

η B S , d c :

Discharging efficiency of the BS (%)

PSO:

Particle Swarm Optimization

PV:

Photovoltaic

SPSO:

Standard PSO

ST:

Simulation time

TEE:

Technical, economic, and environmental

TAC:

Total annual cost

TEP:

Total annual energy production

O&M:

Operation and maintenance

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  1. College of Information Science and Technology, Henan University of Technology, Zhengzhou, 450000, Henan, China

    Yujie Chen & Shuo Zhang

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Yujie Chen: Project administration; Investigation; Writing; Methodology. Shuo Zhang: Writing—review & editing; Data curation; Investigation, Methodology.

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Chen, Y., Zhang, S. Technical, economic, and environmental assessment of a stand-alone power system based on diesel engine with/without energy storage using an optimization algorithm: A case study in China. Environ Sci Pollut Res (2023). https://doi.org/10.1007/s11356-023-31488-3

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